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IJABME

ISSN 1906 - 4063

International Journal of

Applied Biomedical Engineering

Vol. 2, No. 2, July-December 2009

Editorial Board ……………………………………………………………………………

Message from the Editor-in-chief ……………………………………………………… iii

_______________________________________________________________________

INVITED PAPERS

Effect of Transcranial Magnetic Stimulation (TMS) on Visual Search Task

……………………………………………………………………………………………… K. Iramina 1

Development of Virtual Palpation System using Ultrasonic Elastography

………………………………………………………………………………………… K. Hamamoto 9

_______________________________________________________________________________

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Energy Packing Efficiency Based Threshold Level Selection for DTW ECG Compression

………………………………………………………………………… Y. Chompusri and S. Yimman 19

No Visual Mismatch Negativity (MMN) for Simultaneously Presented Audiovisual Stimuli:

Evidence from Human Brain Processing

…………………………..…………………………………………………………… W. Sittiprapaporn 29

A Diagnosis of Tonsillitis using Image Processing and Neural Network

………………………………………………………………… A. Leelasantitham and S. Kiattisin 36

Treatment Planning Technique Applying the Combined CT Imaging and Computational

Fluid Dynamics (CFD) Analysis

………………….……. K. Hemtiwakorn, N. Phoocharoen, V. Mahasittiwat and M. Sangworasil 43

Analysis of Quartz Crystal Microbalance Sensor Array with Circular Flow Chamber

.… ………… K. Jaruwongrungsee, T. Maturos, P. Sritongkum, A. Wisitsora-at, M. Sangworasil

and A. Tuantranont 50

Muscular-Contraction Classification : Comparison Study Between Independent Component

Analysis and Artificial Neural Network

…………………………. D. Sueaseenak, T. Chanwimalueang, W. Iampa, and M. Sangworasil 55

ii

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International Journal of Applied Biomedical Engineering (IJABME)


International Journal of Applied Biomedical Engineering, Vol.2, No. 2 2009

i

International Journal of Applied Biomedical Engineering (IJABME)

Vol. 2, No. 2 (July-December 2009)

Editor in Chief:

Chuchart Pintavirooj, King Mongkut’sInstitute of Technology Ladkrabang (KMITL)

Associate Editors:

Somsak Choomchuay

Supaporn Kiattisin

Supareak Janjarasjitt

Supan Tungjitkusolmun

King Mongkut’sInstitute of Technology Ladkrabang (KMITL)

University of the Thai Chamber of Commerce (UTCC)

Ubonratchathani University (UBU)

King Mongkut’s Institute of Technology Ladkrabang (KMITL)

Editorial Board:

1. Somkiat Wattanasirichaigoon SWU, Thailand

2. Tohru Yagi TIT, Japan

3. John G. Webster Univ. of Wisconcin, USA

4. Ratko Magjarevic IFMBE, Croatia

5. Tsuyoshi Shiina Univ. of Tsukuba, Japan

6. Fernand S. Cohen Drexel U., USA

7. Manus Sanworasil KMITL, Thailand

8. Chusak Limsakul PSU, Thailand

Board of Reviewers:

1. Kazuhiko Hamamoto Tokai Univ., Japan

2. Kosin Chamnongthai KMUTT, Thailand

3. James Goh NUS, Singapore

4. Ian Thomas KKU, Thailand

5. Takafumi Suzuki U. of Tokyo, Japan

6. Kochi Ito Chiba Univ., Japan

8. Shozo Kondo Tokai Univ., Japan

9. Vitoon Leelamanit PSU, Thailand

10.Suradej Tritriluxana

KMITL, Thailand

11. Willis J. Tompkins Univ. of Wisconcin, USA

12. Olivier Adam France

13. Adisorn Leelasantitham UTCC, Thailand

14. Arthorn Sanpanich MU, Thailand

15. Chissanuthat Bunluechokchai KMUTNB, Thailand

16. Mana Sriyudthasak CU, Thailand

17. Sinchai Kamolpivong PSU, Thailand

18. Siridech Boonsang KMITL, Thailand

19. Nuttaporn Pimpha NanoTEC, Thailand

20. Pasin Israsena NECTEC, Thailand

21. Patamaporn Sripadungtham Thailand

22. Phensri thongnopnua CU, Thailand

23. Pornchai Phukpattaranont PSU, Thailand

24. Thurdsak Leuwhathong KMITL, Thailand

25. Saowapak Sotthivirat NECTEC, Thailand

26. Supot Sookpotharom Bangkok Univ., Thailand

27. Warakorn Charoensuk MU, Thailand

28. Watcharachai Wiriyasuttiwong SWU, Thailand

29. Surapan Airphaiboon KMITL, Thailand

30. Wongwit Senawong SWU, Thailand


ii International Journal of Applied Biomedical Engineering, Vol.2, No. 2 2009

Message from the Editor-in-Chief

The IJABME has increasingly drawn attention from many biomedical engineers. As editor-in-chief, I

would like to express my sincere thanks to all the researchers who have submitted their good research

works to be shared and their research result to be exchanged in the current issue of IJABME. Of course,

the success of IJABME would not have been possible without the support and dedication of our

Editorial Board including many colleagues, friends, and professor who have reviewed the manuscripts.

To all of those involved I give my sincere thanks for jobs well done.

Moreover, I would like to take this opportunity to welcome our next guest editor, Professor Kazuhiko

Hamamoto, a distinguish Tokai-University professor in Biomedical Engineering of IEEJ societies and our

long friend and colleague since 2000. The next issue will be distributed in BMEiCON2010 held in Kyoto

University, Japan. I hence encourage all biomedical engineers to wrap-up his/her research work and

prepare the manuscript submitting to IJABME by the end of April. It is a good opportunity to have

their research work shared Asian wide.

Chuchart Pintavirooj, Ph.D.

Department of Electronics

Faculty of Engineering

King Mongkut's Institute of Technology Ladkrabang, Thailand.

E-mail: kpchucha@kmitl.ac.th

Chuchart Pintavirooj was born in Bangkok, Thailand in 1962. He received the B. Sc. (Radiation

Techniques) and M.Sc. (Biomedical Instrumentation) from Mahidol University, Bangkok, Thailand in

1985 and 1989 respectively. In 1995, he received another master degree in Biomedical Engineering

from Worcester Polytechnic Institute, MA, USA. In 2000, he earned a Ph. D. in Biomedical

Engineering from Drexel University, Philadelphia, PA. After working as a research scientist at

Biomedical Instrumentation Department, Mahidol University, he joined Electronic Department,

Faculty of Engineering, King Mongkut’s Institute of Technology at Ladkrabang, Bangkok where he

is currently an associate professor. His current research is in Biomedical Image/ Signal Processing

majoring in Image reconstruction, Image Classification and Image restoration.

Dr. Pintavirooj is the acting chairman of Biomedical Engineering Society of Thailand affiliated with

IFBME.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 1

Effect of Transcranial Magnetic

Stimulation(TMS) on Visual Search Task

K. Iramina, Guest member

ABSTRACT

Transcranial magnetic stimulation (TMS) has

been applied as an important method to investigate

human cognitive process. In this study, we applied

TMS to investigate temporal aspect of the functional

processing of the visual search in the brain. Subjects

were required to respond as quickly and accurately as

possible by pressing a mouse button to indicate the

presence or absence of the target, and the reaction

times were measured. Subjects received four trials

which the TMS stimulus onset asynchronies (SOA)

were set as 100, 150, 200 and 250 ms after visual

stimulus presentation for easy feature, hard feature

and conjunction task, respectively. It was found that

there was a significant elevation in target-present response

time when the TMS pulses were applied 150

ms after visual stimulus presentation. However for

the other SOA cases there was no significant difference

between no-TMS and TMS conditions. Therefore,

we considered that the right posterior parietal

cortex was involved in visual search at about 150 ms

after visual stimulus presentation. When the TMS

applied to the primary visual area on occipital area

50 ms after visual stimulus presentation, there was a

significant decrease in response time. However, there

was no difference at 150 ms SOA.

Keywords: Transcranial Magnetic Stimulation

(TMS), visual search task, visual perception, posterior

parietal cortex(PPC)

1. INTRODUCTION

Transcranial magnetic stimulation (TMS) is the

application of a brief magnetic pulse or a train of

pulses to the skull, which results in an induction of

local electric current in the underlying surface of the

brain, thereby producing a localized axonal depolarization.

TMS has become an important tool for the

study of the functional organization of the human

brain [1]. Earier studies using transcranial magnetic

stimulation were related to the mapping of the cerebral

cortex. Mapping studies were carried out by a

method of localized and vectorial magnetic stimulation

using a figure eight coil [2]. The basic principle

is to concen-trate induced eddy currents locally

Manuscript received on December 16, 2009.,

K. Iramina, Kyushu University, 819-0395 Fukuoka, Japan

Telephone & Fax: +81-92-802-3581

E-mail addresses: iramina@inf.kyushu-u.ac.jp

near a target by a pair of opposing pulsed magnetic

fields produced by a figure-eight coil. This method

facilitates stimulation of the motor cortex of the human

brain within a 5 mm resolution [3][4]. As a noninvasive

and effective method to make reversible lesions

in the human brain, TMS has a long and successful

history. Now it has become a major tool of

cognitive neuroscience and a treatment method for

various neurobehavioral disorders. TMS can be used

to stimulate the cortex to produce visual percepts[5]

or movements[6]. It is more suitable to provide reversible

disruption of activity in the cortex with millisecond

accuracy. Because TMS has high temporal

and spatial resolutions, it can investigate not only

the spatial localization but also the time course of

mental processes. TMS is a noninvasive and effective

method to making reversible lesions in the human

brain. The application of TMS in the investigation

of neurological deficits provides an important method

for investigating human cognitive processes.

Visual search, as a traditionally visual neglect sensitive

measure, was studied by many researchers. Selective

spatial attention [7], memory for the target [8],

object-based atten-tion and identification of the target

are all required in visual search. Much is already

known about the involvement of the large areas of

the visual field [9] [10], the right parietal cortex [11],

right superior temporal gyrus [12] and right posterior

parietal cortex [12] [13].

In contrast to the knowledge of the location of particular

cortex areas involved in the visual search, the

study of temporal aspect of cortex areas is not sufficient.

Ashbridge et al. [14] applied TMS over the subjects’

right parietal visual cortex while they were performing

feature and conjunction visual search tasks.

The same authors found that TMS had no detrimental

effect on the performance of feature search, but

significantly increased the response time in conjunction

search when TMS was applied over the right

parietal cortex 100 ms after the visual stimuli presentation.

However, in that study, the temporal aspect

of the posterior parietal cortex (PPC) in visual

search was not discussed. In Ellison et al.’s repetitive

TMS (rTMS) study [12], they found that TMS

over the right PPC caused a significant increase in

response time in the landmark task and hard conjunction

search task. However, in their study, since

the rTMS was applied before the visual search stimuli

presentation, the involvement of the PPC in visual

search was confirmed but the time course was not in-


2 K. Iramina: Effect of Transcranial Magnetic Stimulation(TMS) on Visual Search Task (1-8)

vestigated. Fuggetta et al.’s study [15] reported a

significant increase in response time for conjunction

task when a single-pulse TMS applied over right PPC

100 ms after the visual stimuli presentation. Ellison

et al. [16] applied 5 pulse 100 ms apart TMS over

right PPC, they found PPC plays significant effect in

conjunction visual search proc-essing.

In this study, to investigate the temporal aspect

of the posterior parietal cortex involved in the visual

search, we used different TMS stimulus onset asynchronies

(SOA) and measured the visual search response

times. The relationship between the SOA and

the response time was investigated. We also investigated

the effect of TMS on primary visual area V1 on

visual search task.

slash (/). In the ’hard feature task’ the target was

a 90 ◦ counterclockwise rotated ‘L’ shape among ‘L’

shape. These two tasks which have only one different

feature between the target and distractor are called

‘pop-out’ tasks. On the other hand, the task which

has more than two different features is called ‘conjunction

task’. In this study, the target was a red

backslash among red and green slash. All items were

subtended in 4.7×4.7 ◦ visual angle on the screen.

Subjects were required to respond as quickly and as

accurately as possible by pressing a mouse button to

indicate the presence or absence of the target. The

target was present on 50% of the trials and there was

never more than one target. Each trial was preceded

by a central fixation cross for 1500 ms, followed immediately

by the stimuli array which was presented

for 1500 ms.

2.2 Magnetic stimulation

The TMS stimulator was a MagStim Super Rapid

Stimulator. Stimulus strength was set as the subjects’

individual resting motor threshold which required to

produce MEPs of >50 µV peak-to-peak amplitude in

at least 6 of 10 successive trials. A figure-of-eight

70mm coil was used. The double coil windings in

the figure-of-eight coil carry two currents in opposite

directions. At the central point of the coil, the

two loops meet and generate a localized summation

of current. The localized electric current induced by

the pulse has low attenuation and high spatial resolution.

When the TMS coil is discharged, a loud click

sound will be produced and this makes the interpretation

of TMS-evoked activity difficult. To investigate

the TMS effect without including auditory artifact,

a control study, no-TMS conditions were applied in

this study. No- TMS condition used two coils. One is

a dummy coil which does not produce the magnetic

field and another coil which are located vertically to

the scalp produces the click sound as shown in Fig.2.

Fig.1: Examples of visual search stimuli (Top: easy

feature, Middle: hard feature, Bottom: conjunction,

Left: target-absent stimulus, Right: target present

stimulus)

2. MATERIALS

2.1 Visual search task

Three visual search tasks were carried out (see Fig.

1). There were eight items in each array. In the ‘easy

feature task’ the target was a backslash (\) among

2.3 Subjects

Eight subjects (one female, seven males, and aged

21-31 years) participated in all the experiments of the

present study, who were experienced psychophysical

observers but ignored the purpose of the experiment.

All were right handed, had normal or corrected-tonormal

visual acuity. All the subjects had previous

practice before the experiment in order to be familiar

with visual search and to make stable response, and

reported a complete absence of epilepsy, or any other

neurological condition in themselves and their known

family history.

3. EXPERIMENTAL PRCEDURE

The time sequence of experiment is shown in Fig.3.

Each trial was preceded by a central fixation cross

for 1500 ms, followed immediately by a visual search


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 3

Fig.2: TMs coil was applied over the right posterior

parietal cortex. (a) TMS condition (b) No-TMS

condition

stimulus, which would be presented for another 1500

ms. Subjects were required to respond as quickly and

as accurately as possible by clicking a mouse button

to indicate the presence or absence of the target (left

button for target present and right button for target

absent). The time from the visual search stimuli

presentation till the button click was recorded as the

response time. Transcranial magnetic stimulations

were applied over the right PPC of the subject at

different time intervals after the visual search stimuli

presentation. These time intervals were called as

TMS stimulus onset asynchronies (SOA).

Fig.3:

No-TMS condition

In the TMS condition, since paired-pulse TMS can

produce larger and longer effects on cortical activity

than single-pulse, paired-pulse TMS was used in the

present study. The highest frequency of TMS device

is 50 Hz, thus, we set inter-stimulus interval=20 ms.

Paired-pulse TMS were applied over the right PPC of

subject. The coil was placed tangential to the surface

of the skull and the centre of the coil was positioned

over electrode site P4 of the international 10-20 system.

The induced current in the brain was parallel

to the Oz-Pz midline, flowed from the occipital

to the frontal cortex. While in the no-TMS condition,

an open circuit coil was put over the subject’s

right PPC just like the TMS condition, while a figureof-eight

coil discharged paired-pulse near to, but directed

away from the subject’s skull. The application

sequences of TMS and no-TMS conditions were randomized

across subjects. Each subject received 60

trials for two times under TMS and no-TMS conditions

as one test. Subjects received four tests with the

TMS stimulus onset asynchronies (SOA) were set as

100, 150, 200 and 250 ms after the visual search stimuli

presentation. Subjects received these four tests

for the easy feature and hard feature search tasks,

respectively. Consequently, subjects received a total

of eight tests in this study. The stimulation protocol

was in accordance with published safety recommendations.

In considering of the safety of TMS, only one

test was performed within one day for each subject.

4. RESULTS

4.1 Visual search task stimulating on the right

PPC

For the easy feature search task, hard feature task

and conjunction task, the target-present response

times at each SOA for each subject are shown in the

Table 1. Obviously, the individual differences among

subjects were large. Moreover, there were differences

in response times between SOA cases even for the

same subject. Since subjects participated all the 4

tests in different days, the mental condition, concentration

and relaxation degrees were different even for

the same subject. This logically led to difference in

the response time. Therefore, it is not appropriate

to use the average of response time of each subject

as the typical value of the experimental data. In the

present study, in order to remove the individual difference

and the difference of experimental conditions, we

used the average of normalized response time as the

typical value of the experimental data. Thus, for each

subject, the average of target-present response time

in the TMS condition was normalized to the no-TMS

condition (set as the baseline=1). The difference between

the average of normalized response time of all

the subjects and the baseline was taken to demonstrate

the TMS effect. Fig.4 shows the target-present

response times and normalized response time at each

SOA cases. In order to investigate the influence of

TMS effects, paired t-test was used to analyze the

difference between the TMS and no-TMS conditions.


4 K. Iramina: Effect of Transcranial Magnetic Stimulation(TMS) on Visual Search Task (1-8)

Table 1: Respose times at each SOA for the easy

feature task, hard feature task and conjunction tasks

of each subjects

We found that, when SOA=150 ms, compared to the

no-TMS condition, there was a significant elevation

in response time when the TMS pulses were applied.

However, for the other SOA cases, there was no significant

difference between the TMS and no-TMS conditions.

For the hard feature search task, the targetpresent

response times and the difference between the

average of normalized target-present response time of

all the subjects is shown in Fig.5. Based on the paired

t-test analysis results, we found that, when SOA=150

ms, compared to the no-TMS condition, there was a

significant elevation in response time when the TMS

pulses were applied. However, for the other SOA

cases, there was no significant difference between the

TMS and no-TMS conditions. For the conjunction

task, the target-present response times and the difference

between the average of norma-lized targetpresent

response time of all the subjects and is shown

in Fig.6. When SOA=150 ms, compared to the no-

TMS condition, there was an elevation in response

time when the TMS pulses were applied. For the

target-absent condition, there was no significant difference

between the TMS and no-TMS conditions for

all three visual search task and the SOA cases.

Fig.4: (a) Target-present response times (b) Normalized

target-present response times at each SOA for

the easy feature visual search task when the right PPC

was stimulated.

4.2 Visual search task stimulating on the right

V1

In order to investigate the difference between the

role of right PPC and the primary visual cortex V1,

TMS was applied to the right V1 at 50ms SOA, 100ms

SOA and 150ms SOA. For the hard feature search

task, the tar-get-present response times and the difference

between the normalization of response time

ratio TMS to no-TMS condition is shown in Fig.7.

When the TMS applied to the right V1 after visual

stimulus presentation at 50 ms SOA, there was a significant

decrease in response time. However, there

was no difference at 150 ms SOA.

4.3 Simple reaction task

In this experiment, we investigated the simple reaction

time. No-targeted hard feature visual search

task was used in this experiment. The subjects re-


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 5

Fig.5: (a) Target-present response times (b) Normalized

target-present response times at each SOA for

the hard feature visual search task when the right PPC

was stimulated.

Fig.7: (a) Target-present response times (b) Normalized

target-present response times at each SOA for

the hard feature visual search task when the right V1

area was stimulated.

Fig.6: (a) Target-present response times (b) Normalized

target-present response times at each SOA for

the conjuncion visual search task when the right PPC

was stimulated.

Fig.8: (a) Response times (b) Normalized response

times at each SOA for the simple reaction task when

the right V1 area was stimulated.


6 K. Iramina: Effect of Transcranial Magnetic Stimulation(TMS) on Visual Search Task (1-8)

Fig.9: (a) Response times (b) Normalized response

times at each SOA for the simple reaction task when

the right PPC was stimulated.

spond the visual stimulation sa soon as possible without

judgement of the presenttation. Stimulus points

of TMS were right PPC or right V1. Fig. 8 shows

the normalization of response time ratio TMS to no-

TMS condition of all the subject when the right V1

was stimulated at 50 ms or 100 ms SOA. Fig. 9 shows

the normalization of response time ratio TMS to no-

TMS condition of all the subjects when the right PPC

was stimulated at 50 ms or 150 ms SOA. When the

right V1 was stimulated at 50 ms SOA, there was a

significant decrease in response time. However, SOA

is 100 ms, there was no difference. When the right

PPC was stimulated at 50 ms SOA, there was a significant

decrease in response time. However, SOA is

100 ms, there was no difference.

5. DISCUSSION

Two previous studies have investigated the involvement

of the PPC in visual search task using

TMS[12, 14]. However, the former used repetitive

TMS (rTMS) before visual search stimuli presentation.

That study indicated that the PPC is involved

in visual search, however the temporal aspect of the

PPC involved in visual search was not discussed. The

other one used single pulse TMS but reported TMS

over the PPC had no detrimental effect on the performance

of feature search. In the present study, to

investigate the temporal aspect of the PPC involved

in feature search, a paired-pulse TMS was applied

over the right PPC, and the time intervals between

visual search stimuli presentation and TMS stimuli

were set as four types, i.e., SOA=100, 150, 200 and

250 ms. Several past TMS experiments on the PPC

showed that TMS over the PPC does not induce eye

movement [14, 17]. Furthermore, some past studies

suggested that the PPC plays a role in the saccadic

eye movement beyond 200 ms after target presentation[18].

In our study, we found a significant influence

in response time when SOA=150 ms; Based on

the above-mentioned studies, we considered that the

saccadic eye movement was not bound at all at 150

ms after visual search task presentation. This lead to

the conclusion that the disruption of eye movement,

which is induced by the TMS, is not the key factor

for the elevation in response time when SOA=150 ms.

A common knowledge of TMS experiments is that

TMS can either excite the cortex or disturb its function.

The observed excitatory effects are normally

muscle twitches or phosphenes, whereas in the “lesion”

mode, TMS can transiently suppress perception

or interfere with task performance. An appropriately

applied TMS pulse delivered in time and space can

transiently disrupt the function of a certain cortical

area at a given time, creating a temporary and localized

cortical “virtual brain lesion”. Thus, TMS can

be used to disrupt the brain activation at a specified

cortical area at a given time point. Therefore, TMS

can be used to investigate not only the spatial but also

the temporal characteristics of task performance. In

the present study, compared to the sham TMS condition,

since a significant elevation in response time

was only observed when SOA=150 ms, we concluded

that the feature search was probably processed in the

PPC at about 150-170 ms after stimulus presentation.

Based on the past conjunction search research[14] and

present study, it seems reasonable to consider that

the PPC plays a dominant role not only in the conjunction

search but also in the feature search tasks.

On the other hand, for the target-absent condition,

it was found that there was no significant difference

between TMS and sham TMS conditions for all the

SOAsettings in both easy and hard feature search

tasks. Based on the actual experimental experience,

we considered that the response of “target present”

or “target absent” is different to that of “where is the

target” in the visual search task. i.e., detection of

target and localization of target are two different processes.

When the primary visual area V1 was stimulated

in the hard feature task, response time shows

the significant decreasing in the case of 50 ms SOA

and 100 ms SOA. For the simple reaction experiment,

when the primary visual area V1 was stimulated, response

time shows the decreasing significantly in the

case of 50 ms SOA. There is no difference in 100 ms

SOA. When the right PPC was stimulated, There

was no significant difference of response time in the

case of 150 ms SOA. These results suggest that right


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 7

PPC plays a dominant role in spatial processing during

visual search. This TMS study opens up a new

possibility for understanding not only the physiological

role of the PPC in the visual search but also the

temporal aspect of the PPC involved in the visual

search. We expect that this study will contribute to

the theories about the visual search dynamics.

6. CONCLUSION

We concluded that it is most difficult to find the

target in the conjunction task. Furthermore, compared

with easy feature task, we found that it is

more difficult to find the target in the hard feature

task. In easy and hard feature tasks, when SOA=150

ms, compared to no-TMS condition the response time

of the TMS condition were increased. Especially in

the hard feature task, there is a significant difference

between TMS and no-TMS conditions. However, in

the other SOA cases of all tasks, we could not found

such TMS effect. Paired t-test was used to examine

whether there is a significant difference between TMS

and no-TMS conditions or not. There was a significant

difference between TMS and no-TMS conditions

when SOA=150 ms in easy feature tasks, hard feature

tasks (p


8 K. Iramina: Effect of Transcranial Magnetic Stimulation(TMS) on Visual Search Task (1-8)

K. Iramina received his Ph.D degree

in Engineering from Kyushu University,

Japan, in 1991. From 1991 to 1995,

he worked at the Department of Electronics,

Kyushu University. In 1996, he

moved to the University of Tokyo, Japan

and he has been an associate professor of

Institute of Biomedical Engineering, the

University of Tokyo. In 2005, he moved

o Kyushu University as a professor of

Graduate School of Information Science

an Electrical Engineering. He has been also a professor of

Graduate School of Systems Life Sciences in Kyushu University.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 9

Development of Virtual Palpation System

using Ultrasonic Elastography

K. Hamamoto, Guest member

ABSTRACT

“Virtual palpation system”, which can present a

tactile sense as user palpates a patient in virtual space

based on the patient’s elasticity information obtained

by an ultrasonic elastography is proposed. This system

uses 3-dimentional haptic rendering technique.

In this research, it is attempted to determine parameters

which are used to realize the same tactile sense

as one in actual palpation. The parameters are determined

experimentally using some breast phantom.

Keywords: Virtual reality; Palpation; Elastography;

Ultrasonic imaging; Haptic rendering

1. INTRODUCTION

ULTRASONIC imaging is widely applied and indispensable

technique in medical diagnostic field currently.

It has many advantages compared with X-

ray imaging, MRI and so on. Unfortunately, however,

it has a serious weak point, where ultrasonic

echo image is not quantitative. Therefore, there are

many researches about quantitative image construction,

which is called “Tissue Characterization”.

Ultrasonic elasticity imaging is one of the most

important tissue characterization researches recently.

HITACHI Medical Corporation has already produced

the technique on a commercial basis and the equipment

is mainly used to decide whether a tumor (for

example, in breast) is benign or malignant [1]. The

newest equipment enables to display elasticity image

in real time. As the technology advances, medical

doctors and ultrasonographers are expecting to display

the elasticity information as not only a visual

image on 2D monitor but “the sense of touch”. That

is to say, they expect “virtual palpation system”. Virtual

Palpation System enables medical doctor to get

the sense of touch of tissue even if it is deep seated

tumor. This system is provided as man-machine interface

between medical doctor and real-time tissue

elastography.

In recent years, virtual reality technique is applied

to medical field, especially, virtual surgery simula-

Manuscript received on December 29, 2009.,

Kazuhiko Hamamoto, Department of Information Media

Technology, School of Information and Telecommunication

Eng., Tokai University, 2-3-23 Takanawa, Minato-ku, Tokyo,

108-8619 Japan Telephone: +81-3-3441-1171 Fax: +81-3-3447-

6005

E-mail addresses: hama@keyaki.cc.u-tokai.ac.jp

tor [2][3]. And I have already introduced virtual reality

application for medical ultrasonic diagnosis in

ref[4]. In ref[4], virtual palpation system has been

also introduced. The virtual palpation system uses

PHANToM DeskTop TM , produced by Sensable Technologies

Inc, which is the most popular haptic device.

This device presents the sense of touch (reaction

force) on a point in virtual space with the point’s

elasticity by Spring-Damper theory. That means a

technique is strongly required which can calculate

accurate elasticity on the pressed point considering

surrounding three-dimensional elasticity information

and the depth of the press. This process is called

“haptic rendering”. This rendering process is very

important because human body is not homogeneous

and has non-linear characteristic. Three-dimensional

finite element method is usually used for the calculation

to get strict accuracy of human haptic sense

[5]. However, it takes high computational cost and

cannot realize a real-time virtual palpation system.

In this research, volume rendering technique in

Computer Graphics has been applied to haptic rendering

[6][7]. It enables to reproduce the human haptic

sense of real palpation approximately and easily

in real time without high computational cost. Of

course, finite element method, etc may be needed

for ideal and precisely accurate virtual palpation system.

However, they take high computational cost and

cannot provide real time system. In this proposed

method, real time system can be provided and it enables

to reproduce real haptic sense approximately

by setting some appropriate parameters. The parameters

are decided from phantom experiments.

In this paper, the setting of parameters is investigated.

Four phantoms which have different

elasticity are made from vinyl chloride material or

polyurethane material. The parameters are set as the

sense of touch in virtual space becomes the same as

the sense of touch of the phantom. Whether the parameters

are unique for the virtual palpation system

or depend on users’ characteristics are investigated. If

the parameters depend on users’ characteristics, calibration

process for each user might be needed. Such

problem for practical use is also investigated.

2. PROPOSED HAPTIC RENDERING

2.1 Transform of Elasticity for PHANToM

In this process, elasticity information which is

measured quantitatively is transformed. This pro-


10 K. Hamamoto: Development of Virtual Palpation System using Ultrasonic Elastography (9-13)

cess enables the elasticity information to be used in

PHANToM DeskTop TM . A parameter used in this

process is called “Elasticity Information Parameter

(EIP)”. This parameter is set by a user according to

the real sense of touch. A user adjusts EIP as the

virtual sense of touch becomes the same as the real

sense of touch.

2.2 Transform of Elasticity for depth

Reaction force which is felt when a user presses a

surface depends on the depth of the press. In addition,

elasticity information which is seated closely to

the surface affects the reaction force significantly and

effect of elasticity information which is seated deeply

to the reaction force is small.

In this process, elasticity information is transformed

according to the depth of the press by eqn.(1).

Fig.1: A phantom with thin strings embedded in a

lattice for investigation on effectiveness of surrounding

elasticity information of the pressed point.

E ′ (x, y, d) =

E(x, y, d)

p d (1)

d : the depth of the press

E ′ (x, y, d) : transformed elasticity at depth d

E(x, y, d) : elasticity at depth d after II(A) process

p : depth parameter (DP )

Fig.2:

A part of Displacement Filter

DP is also set by a user in the same way as EIP.

2.3 Displacement Filter

The reaction force is affected by not only elasticity

information along the direction of the press but

the surrounding elasticity information of the pressed

point. The effectiveness of the surrounding elasticity

information is investigated by using a phantom made

from vinyl chloride material whose elasticity is close

to one of human body. Some thin strings are embedded

in the rectangular prism phantom in a lattice

shown in Fig.1(a). The deformation of the strings

is measured when the phantom is pressed shown in

Fig1(b). The effectiveness is determined by the deformation

experimentally. The estimated effectiveness

is shown in Fig.2. Z direction is the direction of the

press, and the left end column of the top row is the

position of the press. This effectiveness table is called

“Displacement Filter (DF)”.

DF consists of 2 filters. One is for x-z plane and

the other is for y-z plane. That is to say, DF is expressed

in DF(x, z) and DF(y, z). If the depth of the

press is “4”, 4 rows and 5 columns of DF are used.

The accurate elasticity E” on the pressed point considering

surrounding three-dimensional elasticity information

and the depth of the press for PHANToM

DeskTop TM is calculated in eqn.(2).

E ′′ =

4 ∑

4∑

|x|=0 |y|=1 z=0

d∑

{E ′ (x, z) • DF (x, z)

+E ′ (y, z) • DF (y, z)}

(2)

E ′′

: elasticity at the position of the press

for P HANT oM Desktop T M

2.4 Calculation of the reaction force

Finally, the reaction force F presented by PHAN-

ToM DeskTop TM is calculated in eqn.(3).

F = E ′′ × d (3)

This haptic rendering method can produce the accurate

touch of the sense approximately by controlling

parameters, EIP and DP in real time.

3. EXPERIMENTS

3.1 Experimental condition

It is required to set two parameters, EIP and DP

properly to present the touch of sense for virtual

palpation system, which is the same as one of real

palpation. In this paper, the setting of EIP and

DP are investigated experimentally. In this experiment,

PHANToM DeskTop TM is used as haptic device,

which is shown in Fig.3

First, 4 phantoms whose elasticities are different

each other are made. Next, a subject of the experiments

touches the phantoms by a stylus which

is similar to one of PHANToM DeskTop TM . At a

time, the subject touches virtual object by PHAN-

ToM DeskTop TM . The subject adjusts EIP and DP

as the virtual sense of touch becomes the same as the

real sense of touch of the phantoms. Results obtained

by some subjects are compared and investigated.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 11

3.4 Experiment-1

In this experiment, both EIP and DP are variable.

The number of subject is 15 which consist of 10 males

and 5 females in 20 years old generation.

The result is shown in Fig.5. The right side longitudinal

axis shows DP and the left side one shows

EIP.

Fig.3:

PHANToM DeskTop TM

3.2 Phantoms for experiment

Four kinds of phantom are made for this experiment.

The specifications are shown in Table 1.

Fig.4 shows a comparison of elasticity between the

phantoms and tissues of female breast. Phantom1,2

and 3 correspond to normal tissue or benign tumor.

Phantom4 corresponds to malignant tumor. Therefore,

it is important problem that a subject can distinguish

phantom4 from phantom1,2 and 3 by the

proposed virtual palpation system or not.

Fig.5: The right side longitudinal axis shows DP

and the left side one shows EIP.

Fig.6:

DP setting in the case where EIP is fixed.

Fig.4: Comparison of elasticity between phantoms

and female breast tissues.

3.3 Virtual object

The size of virtual object is 34×34 in x-y plane

and 18 in depth. The resolution (voxel size) is 2.5mm

cube. The size of DF in depth is 10. That means a

subject can press the virtual object until 25mm in

depth. The resolution (2.5mm) is determined from

a human sense where the distance in which human

sense can distinguish two points is 2-4mm.

All subjects could set EIP and DP as the virtual

sense of touch of PHANToM DeskTop TM becomes the

same as the real sense of touch of phantoms. However,

the averaged value doesn’t correlate to elasticity

of phantoms in the case of both EIP and DP. Furthermore,

the standard deviation of EIP is large. That

means the parameter setting depends on user. This

result causes difficulty to set the parameters and to

realize the proposed virtual palpation system.

3.5 Experiment-2

The reason why such result is obtained in

Experiment-1 is estimated to be high correlation between

EIP and DP and different standard value for

each subject. Therefore, standard value for either

EIP or DP is defined and fixed in Experiment-2. Realization

of the proposed virtual palpation system is

attempted by one parameter setting.


12 K. Hamamoto: Development of Virtual Palpation System using Ultrasonic Elastography (9-13)

Fig.7:

1.168.

EIP setting in the case where DP is fixed in

DeskTop TM becomes the same as the real sense of

touch of phantoms as well as previous experiments.

The result of EIP setting is shown in Fig.7. As the

result shown in Fig.7, EIP setting corresponds to elasticity.

Larger elasticity phantom has, larger EIP setting

user selects. And its variance is also small. This

result suggests that the proposed virtual palpation

system can be realized by EIP setting according to

elasticity measured by ultrasonic elastography without

a parameter optimization (calibration) for each

user.

However, the variance in the case of phantom4 is

significantly larger than others. The border of benign

tumor and malignant tumor is seated between

phantom3 and phantom4 shown in Fig.4. Therefore,

whether this border can be recognized or not is very

important. This concern is investigated by using t-

test.

As the result of t-test, it is confirmed that there

is significant difference between phantom3 and phantom4.

The result of comparison of a confidence interval

in t-test is shown in Fig.8. This result shows that

phantom 3 and phantom4 are obviously distinguished

on the confidence interval in 95%.

Fig.8:

Comparison of confidence intervals in t-test.

3.5.1 The standard value for EIP

In this experiment, EIP is fixed for each phantom

and only DP can be adjusted by a subject. The subjects

consist of 6 males and 2 females in 20 years

old generation. The standard values of EIP for each

phantom are follows:

phantom1 : 0.0215

phantom2 : 0.0303

phantom3 : 0.0521

phantom4 : 0.1093

All subjects could set DP as the virtual sense of

touch of PHANToM DeskTop TM becomes the same

as the real sense of touch of phantoms. The result of

DP setting is shown in Fig.6.

Unfortunately, the DP setting doesn’t correlate to

elasticity of phantoms as well as Experiment-1. However,

since the DP setting tends to be constant value

and not to depend on elasticity and subject, it is estimated

that DP should be fixed in a constant value.

3.5.2 The standard value for DP

In this experiment, DP is fixed in a constant value,

1.168 and only EIP can be adjusted by a subject. The

constant value for DP doesn’t depend on elasticity

and subject. The subjects consist of 6 males and 3

females in 20 years old generation. All subjects could

set EIP as the virtual sense of touch of PHANToM

3.6 Discussion

As the results of these experiments, it is shown

that the proposed virtual palpation system can be

realized by fixed DP and setting EIP from elasticity

information measured by ultrasonic elastography

without dependence on user.

4. CONCLUSION

The virtual palpation system has been proposed.

The system uses elasticity information measured by

ultrasonic elastography and can present the virtual

sense of touch which is the same as the real sense of

touch in practical palpation. In this paper, a new

haptic rendering method is proposed for the virtual

palpation system. The haptic rendering method enables

to realize re-producing elasticity of the point

of press for PHANToM DeskTop TM in real-time with

considering the depth of press and effect of three dimensional

distribution of elasticity information. In

this method, two parameters setting are needed, EIP

and DP. The results of experimental investigations

show that the proposed virtual palpation system can

be realized by fixed DP and setting EIP only from

elasticity information measured by ultrasonic elastography

without dependence on user.

There is remained evaluation of the proposed system

by medical doctor in the future.

References

[1] J. C. Bamber, P. E. Barbone, N. L. Bush, D.

O. Cosgrove, M. M. Doyely, F. G. Fuechsel, P.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 13

M. Meaney, N. R. Miller, T. Shiina & F. Tranquart,

“Progress in Freehand Elastography of

the Breast,” IEICE Trans. Inf. & Syst., Vol.E85-

D, No.1, pp.5-14, 2002.

[2] N. Suzuki, A. Hattori, A. Takatsu, A. Uchiyama,

T. Kumano, A. Ikemoto & Y. Adachi, “Virtual

surgery simulator with force feedback function,”

Proceedings of the 20th Annual International

Conference of the IEEE Engineering in Medicine

and Biology Society, Vol.20, No.3, pp.1260-1262,

1998

[3] M. Nakao, M. Komori, H. Oyama, T. Matsuda,

G. Sakaguchi, M. Komeda & T. Takahashi,

“Haptic Reproduction and Interactive Visualization

of a Beating Heart Based on Cardiac Morphology,”

2001 International Medical Informatics

Association, MEDINFO 2001, eds. V. Patel,

et al., IOS Press. Amsterdam, pp.924-928, 2001.

[4] K.Hamamoto, “Virtual Reality as Human Interface

and its application to Medical Ultrasonic

diagnosis,” International Journal of Applied

Biomedical Engineering, Vol.1, No.1, pp.14-

19, 2008.

[5] S. Sarama, “Virtual Haptics,” Proceedings of the

9th IVR seminar, pp.75-104, 2001.

[6] K. Hamamoto, M. Nakanishi & T. Shiina, “Investigation

on Haptic Display System for Medical

Ultrasonic Elasticity Imaging,” Modelling in

Medicine and Biology VI, Editors : M.Ursino,

C.A.Brebbia, G.Pontrelli and E.Magosso, WIT

PRESS, pp.591-598 Sep. 2005 [Proceedings of

6th International Conference on Modelling in

Medicine and Biology].

[7] K.Hamamoto, “Investigation on Virtual Palpation

System using Ultrasonic Elasticity Imaging,”

Proceedings of the 2006 IEEE Engineering

in Medicine and Biology 28th Annual Conference,

pp.4873-4876, 2006

Kazuhiko HAMAMOTO was born

in Nagasaki prefecture, Japan in 1966.

He received B.D, M.D and Ph.D from

Tokyo University of Agriculture and

Technology in 1989, 1991 and 1994 respectively.

He was assistant professor in

Dept. of Communications Eng., Tokai

University in 1994, Associate Professor

in 1999, and Professor in Dept. of

Information Media Technology, School

of Information and Tele-communication

Eng., Tokai University in 2009 and now. His research interest is

information architecture, especially, medical image processing,

human interface and virtual reality. He joins IEICE, IEEJ,

IEEE, VRSJ, etc. He is a committee member of Technical

Committee of Medical and Biological Engineering, Society of

Electronics, Information and Systems, IEEJ


14 K. Khampitak et al: Metal Guarder Could Prevent the Spread of Tissue Desiccation ... (14-18)

Metal Guarder Could Prevent the Spread of

Tissue Desiccation: A Preliminary in Vitro

Double Blinded Study.

K. Khampitak ∗ , T. Khampitak,

S. Taechajedcadarungsri, and K. Seejorn, Guest members

ABSTRACT

Nowadays the bipolar electro-coagulator is widely

used in gynecologic surgery. A major problem with

using this instrument was the uncontrolled extension

of tissue desiccation, causing organ injuries. This experiment

was designed to study the effect of metal

guarder on the spread of tissue desiccation compared

with the non guarding side. The experiment was divided

into two parts according to method of measurement.

The first was use of fresh raw meats and measurement

by visual inspection which mimicked the

real practice. The ten specimens were desiccated by

5-mm bipolar electro-coagulator (40 W). The mean

spread of uncontrolled tissue desiccation in the metal

guarding side was 1.4 mm. (range 1-3 mm) and non

metal guarding side was 4.9 mm. (range 2-7 mm.)

which the difference was statistically significant by

Wilcoxon Signed-Rank test. The second part was

made in the same way as the previous except for the

animal specimens and measuring method. Ten pieces

of very fresh rat meat, 3 mm thick, were prepared

and desiccated. The spread of tissue desiccation was

measured on histological section. The mean spread of

uncontrolled tissue desiccation in the metal guarding

side was 1.1 mm. (range 0.6-1.5 mm.) and non metal

guarding side was 4.1 mm. (range 2.5-9.1 mm.). The

differences was statistically significant by Wilcoxon

Signed-Rank test. We believe that this result can lead

the surgeon’s technique for the thermal safe electrosurgery

or can be a new concept for innovation of a

safety bipolar electro- coagulator.

Keywords: bipolar coagulation, thermal damage,

electro-surgery.

1. INTRODUCTION

Recently, bipolar electro-coagulator has been introduced

as an electrosurgical vessel sealing device.

Nowadays, it has been widely used in abdominal,

vaginal and laparoscopic hysterectomies with the advantages

of reducing surgical blood loss and operating

* Corresponding author.

Manuscript received on January 8, 2009.,

K. Khampitak, Department of Obstetrics and Gynecology

Faculty of Medicine , Khon Kaen University, Thailand.

E-mail addresses: kovit@kku.ac.th

period [1, 2]. The newly bipolar electro-coagulator

with audible tone indicating complete of the process

is able to seal vessels up to 7 mm. in diameter [3-6].

However, the risk of uncontrolled thermal injury from

high temperatures at the instrument tip has been the

major problems in iatrogenic thermal injury.

The living tissue is slowly heated when the temperature

above 50 ◦ C. If the tissue is heated to 90 ◦ C,

irreversible cellular water completely evaporates (desiccation).

The cell walls are ruptured at 100 ◦ C (vaporization)

and tissue begins to carbonize and char at

250 ◦ C.[7,8]. In the operative field, the uncontrolled

tissue desiccation could spread (above 60 ◦ C) up to 10

mm. from the tip of the instrument [9, 10].

However we have found atraumatic grasping forceps

adjacent to bipolar forceps can minimize thermal

injury during electrosurgery. To investigate this

effect, we designed a study by using fresh animal tissue

and measurement by visual inspection and histological

analysis.

2. MATERIALS AND METHODS

2.1 Experimental design

The experiment was divided into two parts according

to the measurement methods. Each animal tissue

was grasped by Kleppinger bipolar electro-coagulator

and the laparoscopic grasping forceps, which grasped

laterally as experimental side while the other was left

as control side. The data was then subjected to statistical

analysis.

2.2 Procedures

In the first experimental part, the fresh raw meat

with 3 mm. thickness was grasped by Kleppinger

bipolar forceps while laparoscopic grasping forceps

was being grasped laterally. The tissue desiccation

started when 40 W electric power of bipolar electrocoagulator

was turned on. The electricity was run

until no air bubble on the tissue surface (tissue desiccation).

Then, the extension of tissue desiccation was

evaluated by visual inspection using measuring ruler

which mimicked the real tissue inspection in the operative

field. (Figure 1 and 2)

The second part was made in the same way as the

previous except for the animal specimens and measuring

method. Since the visual measurement alone


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 15

Fig.1: A piece of meat was desiccated by bipolar

electro-coagulator (40 W) while grasping forceps were

grasping at the side of the bipolar electro-coagulator

as a metal guarder.

Fig.3: Normal muscle tissue is shown on the right

side of the picture. On the left side, the desiccated

area is obviously eosinophilic with glassy appearance.

Fig.2: The spread of uncontrolled tissue desiccation

in the metal guarding side (arrow) was lesser than

non metal guarding side.

was not sufficient to fully represent the histological

differences, then we decided to measure on microscopically

histological section and used fresh rat meat

as the experimental specimens. After ending of each

one, the tissue was immediately placed into a 10%

formalin-buffered solution. After 24 hours, the samples

were embedded in paraffin, sectioned and stained

with hematoxylin/eosin. Then, the section was evaluated

microscopically for the whole area where thermal

effects were evident-was measured, in mm, using

a built-in stage micrometer calibrated.

The experiment was performed at the Srinagarind

Hospital and the study was approved from the Animal

Care and Use Committee of Khon Kaen university,

Thailand.

2.3 Statistical analysis

On the study design, each specimen was measured

for both control and experiment sides, then it could

Fig.4: The cytoplasm of desiccated muscle cells

(Right Pict.)is glassy and eosinophilia with partial

loss of striation. The nuclei are condensed and deep

basophilic. The normal muscle cells (Left Pict.), on

the contrary, retain striation and detailed nuclei.

prevent intervariable influences that might have induced

a confounding bias. To abolish of personal bias,

we use double blinded technique for our pathologist.

Finally, the spread of uncontrolled tissue desiccation

in the metal guarding side and the non metal guarding

side of each experimental part was statistical analyzed

by Wilcoxon Signed Rank test.

3. RESULTS

Ten pieces of fresh raw meat were desiccated by

Kleppinger bipolar electro-coagulator (40 W) while

the grasping forceps were grasping beside the bipolar

electro-coagulator as a metal guarder. The length

of laterally uncontrolled tissue desiccation was measured

in both sides.

The results of first part were showed in the Table 1.

The mean length of uncontrolled tissue desiccation in


16 K. Khampitak et al: Metal Guarder Could Prevent the Spread of Tissue Desiccation ... (14-18)

the metal guarding side (group 1) 1.4 mm. (range 1-3

mm) and non metal guarding side was 4.9 mm. (range

2-7 mm.). There was significant difference between

the two groups according to Wilcoxon Signed Rank

test.

Ten pieces of fresh rat meat were desiccated in the

same way as the previous The results of laterally uncontrolled

desiccated length were showed in the Table

2. The mean length of uncontrolled tissue desiccation

in the metal guarding side (group 1) was 1.1 mm.

(0.6-1.5 mm.) and non metal guarding side (group 2)

was 4.1 mm. (2.5-9.1 mm.). There was also significant

difference according to Wilcoxon Signed Rank

test.

Table 1: The results of first experimental part,

group 1 was the spread of uncontrolled tissue desiccation

data of the metal guarding side and group was

the data of non metal guarding side, using visual inspection

(measuring ruler).

Number of Group Group

Experiments 1(mm.) 2(mm.)

1 1 4

2 2 5

3 1 2

4 1 6

5 1 7

6 1 5

7 3 5

8 1 5

9 2 5

10 1 5

Statistical significance P< .001.

4. DISCUSSION

The development of laparoscopic surgery has stimulated

substantive research and development in the

medical devices industry for the progression in the

technologies of energized dissection. With the advantages

of reducing surgical blood loss and operating

period, nowadays, electro-coagulator has been widely

used in all routes of surgery, including laparoscopic

hysterectomies. [1, 2] ,[11]

The incidence of iatrogenic complications caused

by laparoscopic electro-surgery is 2 to 5 per 1,000

[12,13]. Most are thermal injuries result from electrocoagulator.

Major of these are caused by the

stray current injuring adjacent structures, such as

the bowel, bladder, and ureter. Bipolar electrocoagulator

can better control this unwanted stray current

[14,15].

The mechanism of bipolar electro-coagulator is to

form vapor pockets coalesce and then become the vaporized

zones at the areas of highest power density

along with a proximal thrombus, causing hemostasis.[16]

However, thermal damage may occur well beyond

their confines. The unabated application of current

can propagate a thermal bloom that disruptively

bubbles steam through the surrounding parenchyma,

damaging tissue at some distance from the target

site.[17] (Figure 5)

Table 2: The result of second experimental part,

group 1 was the spread of uncontrolled tissue desiccation

data of the metal guarding side and group 2 was

the data of non metal guarding side, using microscopically

histological measurement.

Number of Group Group

Experiments 1(mm.) 2(mm.)

1 1.5 3.5

2 0.8 2.5

3 1.2 3.2

4 0.6 2.5

5 1.1 6.1

6 1.2 4.2

7 0.9 9.1

8 1.5 3.5

9 1.2 3.2

10 1 3.1

Statistical significance P< .001.

Fig.5: The mechanism of bipolar electro-coagulator

was to form vapor pockets coalesce and then become

the vaporized zones at the areas of highest power density.

The desiccation caused thermal effect spreading

through the adjacent tissue

A recent study conducted by the Dundee group

on detection of the spread of thermal energy in bipolar

electro-coagulator by using an infrared imaging

system, suggested that a maximum temperature of

150 ◦ C was detected at the instrument tip and a thermal

spread (above 60 ◦ C) was extended up to 10 mm.

from the tip. [7-10]

Remorgida V [18] report that tissue thermal damage

caused by bipolar forceps can be reduced with


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 17

outer plastic shield. However, in practice, plastic

shield can concentrate the heat inside the jaws,

but cannot neutralize the electrical current causing a

thermal bloom from the bubbles steam.

So that we designed this study under the hypothesis

that metal guarder could both neutralize the

spread of electromotive energy and absorb the spread

of thermal energy. Then, it could better reduce the

spread of uncontrolled tissue desiccation which this

experiment showed a better effect of metal guarder

compared to the control part.

Finally, we recommend conducting the experiment

on alive animals to gain data on the effect of this

technique in living tissue.

5. CONCLUSION

Metal guarder could prevent uncontrolled tissue

desiccation from bipolar electro-coagulator. We believe

that this procedure could modified to the thermal

safe surgical technique and lead the inventor to

modify the newly safety bipolar electro-coagulator.

6. ACKNOWLEDGMENT

The authors wish to thanks the Head of the Department

of Obstetrics and Gynecology, Faculty of

Medicine, Khon Kaen University for giving permission

to conduct this study and Ms.Dararat Khampusaen

for checking the English manuscript.

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Inter J of Gynecol Obstet, vol. 91, pp.

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[2] I.E. Petrakis, K.G. Lasithiotakis, G.E. Chalkiadakis,

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Total Abdominal Hysterectomy,” Int J Gynaecol

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[3] B. Levy, L. Emery, “Ramdomized Trail of Suture

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Surg Endosc, vol 15, no 8, pp 799-801, Aug. 2001.

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and Initial Experience of a New Device

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[6] A.I. Brill, “Bipolar Electrosurgery: Convention

and Innovation,” Clin Ostet Gynecol, vol 51, no

1, pp 153-158, 2008.

[7] A. Luciano, R. Soderstrom, D. Martin, “Essential

Principles of Electrosurgery in Operative Laparoscopy,”

J Am Assoc Gynecol Laparosc, vol

1, pp. 189-195, 1994.

[8] A.I. Brill, “Energy Systems for Operative Laparoscopy,”

J Am Assoc Gynecol Laparosc, vol

5, pp. 333-349, 1998.

[9] C. Song, B. Tang, P.A. Campbell, A. Cuschieri,

“ Thermal Spread and Heat Absorbance Differences

Between Open and Laparoscopic Surgeries

During Energized Dissections by Electrosurgical

Instruments,” Surg Endosc, Mar. 2009.

[10] J. Fleshman, “Advanced Technology in the Management

of Hemorrhoids: Stapling, Laser, Harmonic

Scalpel, and lLgasure,” J Gastrointest

Surg, vol 6, no 3, pp 299-301 May-Jun. 2002.

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Kanok Seejorn M.D. Department of

Obstetrics and Gynecology Faculty of

Medicine , Khon Kaen University, Thailand.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 19

Energy Packing Efficiency Based Threshold

Level Selection for DTW ECG Compression

Y. Chompusri ∗ and S. Yimman, Guest members

ABSTRACT

In this research, the compression method of Electrocardiogram

(ECG) signal is proposed. This

method is based on Wavelet Transform with EPE

threshold level selection. Also, the advantage of Dynamic

Time Warping (DTW) is applied in this algorithm

for efficiently compression of ECG signal, particularly

in irregular period signal. There are many

researches working on compression algorithm of ECG

signal. Almost of them have good result in compression

of regular period signal whereas the result is not

satisfied when those techniques are applied to irregular

period signal. DTW technique is applied for this

purpose which replaces of Period Normalization process.

DTW maps ECG signal to the most correlate

point of reference beat. Therefore all beats seem to

have equal length while all signal beats still remain

the same. From DTW, the residual difference value

is decreased that helps to reduce the error which occurring

from Period Normalization process. Discrete

Wavelet Transform (DWT) is applied to decompose

residual signal into wavelet coefficients after that the

redundant information is eliminated by thresholding.

To higher the ratio of compression while remain the

significant clinical information, the threshold level is

chosen based on Energy Packing Efficiency (EPE).

This threshold technique helps to remove the insignificant

information depending on the conserved energy

in each frequency band. The performance is evaluated

on both regular and irregular period records

of MIT-BIH arrhythmia database. This method improves

compression error and moreover, the peak information

which is essential information of ECG is

always reconstructed at the exact position. Also,

the setting of threshold value based on EPE can efficiently

increase the compression ratio higher than the

conventional method.

* Corresponding author.

Manuscript received on September 29, 2009 ; revised on December

28, 2009.

Yotaka Chompusri is with the Instrumentation and Electronics

Engineering Department, King Mongkut’s University

of Technology North Bangkok, 10800, Thailand.

Surapun Yimman is with the Industrial Physic & Medical

Instrumentation Department, King Mongkut’s University of

Technology North Bangkok, 10800, Thailand.

E-mail addresses: ycps@kmutnb.ac.th (Chompusri Y.) and

sym@kmutnb.ac.th (Yimman S.)

Keywords: ECG, Compression, Dynamic Time

Warping, Discrete Wavelet Transform, Energy Packing

Efficiency

1. INTRODUCTION

ECG is one of very importance clinical information

for cardiologist. The disorder of electrical conduction

in heart can be diagnosed as the risk of cardiac arrhythmias

[1]. For diagnosis, the ECG signal will

be recorded, transmitted, monitored and processed.

The memory consuming for ECG processing is high.

Therefore, many methods were invented to reduce

the size of memory consuming for efficiently ECG

processing. Compression methods are done in many

techniques which classified into three main categories,

Parameter Extraction such as vector quantization,

Direct Time Domain technique such as AZTEC and

Transform Domain technique such as wavelet transform

[2-5]. The objective of compression is increasing

Compression Ratio (CR) meanwhile essential information

is not lost in reconstructed signal. Percent

Root Means Square Difference (PRD) is one parameter

to evaluate how well of quality in reconstructed

signal. There is a tradeoff between CR and PRD

that increasing of CR will reduce the quality of reconstructed

signal which is the increasing of PRD. One

of algorithm resulting in good performance both in

CR and PRD is average beat subtraction and residual

differencing method [6]. This method did well on regular

period signal but not for irregular period signal.

That is affected from Period Normalization process

which makes all beats to have equal length. When

the signal is irregular in period, the large difference

between the reference beat and the signal beat occurs.

That is because signal beat is subtracted by the mismatching

value. On this research, the Dynamic Time

Warping (DTW) technique is applied instead of Period

Normalization process in preprocessing method.

This technique matches the signal beat to the most

correlate point of reference beat, so that all signal

beats are subtracted by the closest value. This makes

the residual value to be smaller. Therefore, the DTW

technique helps to reduce the residual value between

signal and reference beat [7]. In consequence, the

error reduces in reconstructed signal and compression

ratio increases. The Discrete wavelet Transform

(DWT) and threshold technique which based on EPE

are applied to increase efficiency in compression ratio.

In conventional threshold technique which based


20 Y. Chompusri and S. Yimman: Energy Packing Efficiency Based Threshold Level Selection for DTW ... (19-28)

on the maximum of wavelet coefficient’s magnitude

is not suitable. From our previous work, when the

threshold value is set to twice, the increasing of compression

ratio is not higher than 0.05 %. In this proposed

method, the threshold value is set based on the

level of reserved energy of wavelet coefficient in each

level of decomposition. This thresholding technique

increases the compression ratio up to 25 %.

2. THE ALGORITHM

Fig. 1 shows process of the proposed compression

method. First, R peak is detected in QRS complex

detection and then reference beat is determined from

searching every beat to find the most repeated period

and each value of reference beat. Afterward signal is

aligned to reference beat by Dynamic Time Warping

technique. The residual difference is computed from

subtracting signal and match point of reference beat.

After that the Discrete Wavelet Transform is used

to decompose signal into wavelet coefficients and the

thresholding process sets zero value to the insignificant

coefficients. Finally, signal is compressed by

encoding.

Fig.2: Block diagram of proposed reconstruction algorithm.

provides 99.7% accuracy of QRS Complex. The period

of ECG signal is defined between R-R interval

which is illustrated in Fig. 3.

Fig.3:

ECG Signal.

Block diagram of proposed compression algo-

Fig.1:

rithm.

In Fig. 2 is the reconstruction process of this algorithm.

The compressed residual and warp path

are decoded and then the Inverse Discrete Wavelet

Transform combines all coefficients into reconstructed

residual signal. In the mean time, the warp path is

calculated back to match the reconstructed residual

signal with the reference beat. Then the matching

point of reference beat is added back to the reconstructed

residual signal.

2.1 Preprocessing

The morphology of ECG is shown in Fig. 3.

Firstly, each beat is split from the whole signal. QRS

complex of each beat is detected using algorithm of

Hamilton and Tompkins [6] which process steps are

band pass filtering, differentiating, squaring, moving

averaging and the peak decision rule. This algorithm

After every beat is found, the reference beat is determined.

The period of reference beat is chosen from

the most frequent period to gain the smallest residual

difference. The most repeated period is searched

from all signal beats and the most frequent period

occurring is defined as the reference period. After

that, each point of reference beat is computed from

averaging each point of every beat which its period

equals to the reference period. In residual difference

method, the residual is calculated from the difference

between the reference beat and signal. Generally, signal

period is normalized to be equal to the period of

reference beat such as extending the original signal

by zero or mean value [8]. For the regular period,

the signal is perfectly reconstructed but not for irregular

period signal. The high residual difference occurs

when normalized period signal is subtracted by

the deviate point of reference beat. The Period normalization

process increases the residual value that

makes more errors in reconstruction. In this paper,

the Dynamic Time Warping technique is presented to

decrease the magnitude of residual value. When the

reference beat is determined from the most frequently


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 21

detected period of ECG signal, then DTW is used to

warp every point of each beat to the reference beat.

2.2 Dynamic Time Warping Algorithm

The Dynamic Time Warping (DTW) technique is

applied to reduce the residual value from residual differencing

process. This technique replaces of the Period

Normalization process. Dynamic Time Warping

is the non linear matching technique and usually used

in speech and pattern recognition. This technique is

the robust measurement and can compare the similarity

between two signals which are not equal in time

series ignoring both the local shift and the global shift

of signal [9]. DTW will match the most correlate

points between compressed or stretched beat to the

reference beat as illustrates in Fig. 4, the upper is the

shorter period beat, the middle is the reference beat

and the lower is the longer period beat, respectively.

This technique maps all signal beats to the most similarity

point of reference beat with no concerning to

the length of beat.

Fig.5:

matrix.

Calculation the optimal warp path from cost

A warp path, W, indicates the matching pair between

signal X and signal Y.

W = w 1 , w 2 , ..., w k , ..., w K ; max(I, J) ≤ K < I + J

(3)

Where K is the length of warp path and the element

of warp path, w k = (i, j), is the matching index.

There are some constraints of warp path, W, that

must begin and stop in diagonally opposite corner of

cost matrix and be monotonically increasing as (4)

and (5). Therefore every index of each time series

must be matched.

w 1 = (1, 1), w K = (I, J) (4)

Fig.4: Matching of similarity between processed beat

to reference beat.

The cost matrix and the optimal warp path are

illustrated in Fig. 5. The warp path is searched after

the adjacent lowest value cell of cost matrix. This

path matches similar points between two beats which

their period lengths are not equal.

The given two time series X and Y with lengths I

and J, respectively.

X = x 1 , x 2 , ..., x i , ..., x I (1)

Y = y 1 , y 2 , ..., y j , ..., y J (2)

w k = (i, j), w k+1 = (i ′ , j ′ ); i ≤ i ′ ≤ i+1, j ≤ j ′ ≤ j+1

(5)

The optimal warp path, W, is only the minimum

of distance warp path, D(w ki , w kj ) , as shown in (6).

[ K

]


W = min D(w ki , w kj )

k=1

(6)

This path could be calculated using dynamic programming

to fill the cost matrix. The value in each

cell of the cost matrix is the minimum warp distance,

D(i, j) of two time series of point i and j as in (7).

D(i, j) = Dist(i, j)

+ min {D(i − 1, j − 1), D(i − 1, j), D(i, j − 1)}

(7)

Where Dist(i, j) is usually the Euclidean distance

between x i and y j .


22 Y. Chompusri and S. Yimman: Energy Packing Efficiency Based Threshold Level Selection for DTW ... (19-28)

On this proposed method, the modified warp path

is computed to any warp path which length, K, is

longer than the length of processed beat, J. In this

case, that warp path is shortened to the length of

the processed beat. If single point of the processed

beat maps to more than one point of the reference

beat, warp pair which gives the minimum Euclidean

distance between two signals is chosen as in (8).

EP E Ci = ĒCi

E Ci

× 100% (10)

Where ĒCi is the total energy of coefficient in level

i after thresholding and ĒCi is the total energy of

coefficient in level i before thresholding.

Table 1 shows the contributing energy in each subband

using three level of decomposition.

w j = min {..., w(i − 1, j), w(i, j), w(i + 1, j), ...} (8)

Then the length of total warp path is equal to the

length of overall signal.

Each element of warp path contains the index of

matching pair between all signal points and reference

beat points.

2.3 Residual Difference

The residual will be calculated from each warp pair

which contains the matching index between processed

beat and reference beat. The warp pair indicates

which point of reference beat will be subtracted from

processed beat to receive the residual difference. The

residual difference is acquired by (9).

Table 1:

Energy contribute in each sub-band level

Residual j = y j − x i ,w k = (i, j) (9)

In this case, the residual value will be small because

each signal point, y j , is subtracted by the closest

value of reference point, x i .

2.4 Discrete Wavelet Transform and Threshold

Residual signal composes of both significant and

redundant information. Discrete Wavelet Transform

(DWT) is used to decompose the residual signal into

sub-band wavelet coefficients. In this research, the

daubechies wavelet is chosen because the daubechies

wavelet shape is compact enough to receive the information

precisely in time [10]. The result of transformation

composes of approximation coefficient and

detail coefficient in each level of decomposition. The

value of coefficient in detail coefficient is relatively

low comparing to the approximation coefficient.

Threshold is applied to mitigate the insignificant

data in each level of decomposition. The coefficient

which its magnitude is lower than significant level is

set to zero value. This mitigation process causes nonsignificant

distortion in reconstructed signal because

of energy invariance property of orthonormal wavelet

transform.

The significant of coefficient is determined by

threshold value which based on Energy Packing Efficiency

(EPE) [11, 12]. The EPE is a percentage

quantity that presents a measure of the total preserved

energy of a certain sub-band after thresholding

with respect to the total energy in that sub-band

before thresholding and is defined as (10)

Most of energy contributes in lower sub-band especially

in approximation coefficient. Energy presenting

in approximation coefficient level, E CA , is varying

from 52% to 96% of total energy which depends

on point number and shape of signal. To prevent the

significant distortion, the approximation coefficient is

preserved by setting high value of EPE. The energy in

detail coefficient level, E CD , is less in higher sub-band

level and the difference of energy between adjacent

sub-band levels is relative small which is not greater

than 5.9% in regular case and 2.7% in irregular case

of the total energy in that sub-band. Therefore, the

same EPE value of each level of detail coefficient is

chosen. The magnitude of detail coefficient is lower

than the magnitude of approximation coefficient and

the number of detail coefficient is larger than that of

approximation coefficient. To achieve of elimination

of redundant information, the EPE of detail coefficient

can be selected in small value. The following

algorithm is defined to calculate the threshold value

[12].

1. Calculate the total Energy, E Ci in each level i of

the wavelet coefficient, X.

E Ci = ∑ X 2 (11)

2. Desire the retained energy in wavelet coefficient

after thresholding, Ē Ci such as ĒCi = 0.99E Ci .


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 23

3. Reorder the wavelet coefficient in descending order

of the magnitude, X s .

4. Use the following pseudo code to compute the

threshold value.

Set energy = 0

Set k = 0

While energy


24 Y. Chompusri and S. Yimman: Energy Packing Efficiency Based Threshold Level Selection for DTW ... (19-28)

Table 2: CR and PRD of proposed method with

100,103 and 105 (regular)

Table 4: CR and PRD of proposed method with

117,119 and 203 (irregular)

Table 3:

CR and PRD of previous method

Therefore, this research focuses on the compression

of irregular period signal such as signal record 117,

119 and 203.

From evaluation of energy in each level of subband,

the energy of approximation coefficient is more

than 82%. Especially in high varying signal, the energy

is about 95%. Therefore, degree of energy contributing

in approximation coefficient indicates the

regularity of signal.

From Table 4, the testing shows the results that

PRD of this proposed algorithm is low because of the

DTW algorithm improving the alignment between

each beat and reference beat to have equal length.

Consequently, error from Period Normalization process

will not occur. For compression ratio, the performance

is better than previous method but still low.

The increasing of CR1 value is small even though the

retained energy is decreased to half. This method

still does not achieve for CR1 because total compressed

data is added up by warp path which data

size is larger than data size of compressed residual

signal. Moreover, threshold setting has no effect on

warp path’s size. Last column of Table 4 shows CR2

value when the warp path is not included in calculation.

The compression ratio excluding warp path,

CR2, is higher than two times of the compression ratio

including warp path, CR1, especially in case of

small EP E DC setting.

The threshold’s effect on irregular period signal is

different from those on regular period signal. When

EP E AC value reduces, it makes small change in

higher compression ratio but large change in higher

PRD, which is more than three times. If EP E DC

value decreases, compression ratio has much increasing

while PRD has not much increasing.

Fig. 9, Fig. 10 and Fig. 11 are the test results of

signal 117, 119 and 203 respectively. The maximum

error is marked at sampling number 400.

There is another achievement of proposed method

that all peaks are always reconstructed at the same

instant as in original signal. This is the benefit from

Dynamic Time Warping technique which maps the

peak of original signal to the peak of reference beat.

Therefore, the peak will be always added back to

where it’s original position in reconstruction process.

From Fig. 9 shows that there is only small error

around peak point when applied this algorithm to

signal record 117. Even though, record 119 and 203

which are highly varying period as shown in Fig. 10

and Fig. 11, all peaks remain in reconstructed signal

and at the exact position.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 25

Fig.6: Record 100 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

Fig.7: Record 103 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

4. CONCLUSION

The aim of this proposed method is to improve the

algorithm of ECG compression especially on irregular

period ECG. Dynamic Time Warping technique

is applied in preprocessing process instead of Period

Normalization. This technique can reduce the residual

value. In consequence, the error between original

signal and the reconstructed signal is enormously

decreased. But there is some disadvantage of this

technique which is the addition warp path producing

from Dynamic Time Warping process. Since the size

of warp path more than two times the size of compressed

residual, that extremely reduces the compression

ratio. To improve compression ratio, the threshold

selection based on EPE is applied and the threshold

value depends on contributed energy in each subband.

The more varying period is the more energy

in low sub-band, particularly in approximation coefficient.

The high EPE value of approximation coefficient

will be selected to keep the essential information

and the low EPE value of detail coefficient will be

chosen to increase the compression ratio while only

small change of error occurs. Another advantage of

this proposed method is the improving of the reconstructed

signal especially around peak point which

is the importance clinical information. This correctness

arises from DTW process which pairs all peaks

to peak of reference beat. Thereby, all peaks will al-


26 Y. Chompusri and S. Yimman: Energy Packing Efficiency Based Threshold Level Selection for DTW ... (19-28)

Fig.8: Record 105 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

Fig.9: Record 117 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

ways be reconstructed from reconstruction process.

No matter to the regularity of signal period, the result

from proposed method shows less distortion than

other methods.

For further work, this research will keep on increasing

the compression ratio by developing of algorithm

which reducing the size of warp path to lower the

total size of compressed signal and by developing algorithm

for choosing the best threshold value based

on Energy Packing Efficiency to receive the optimal

value between compression ratio and error.

References

[1] H. J. L. M. Vullings, M. H. G. Verhaegen, and

H. B. Verbruggen, “Automated ECG segmentation

with dynamic time warping,” in Engineering

in Medicine and Biology Society Proceedings of

the 20th Annual International Conference of the

IEEE, 1998, pp. 163-166

[2] M. L. Hilton, “Wavelet and wavelet packet compression

of electrocardiograms,” Biomedical Engineering,

IEEE Transactions on, vol. 44, pp.

394-402, 1997.

[3] S. M. S. Jalaleddine, C. G. Hutchens, R.

D. Strattan, and W. A. Coberly, “ECG data

compression techniques-a unified approach,”


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 27

Fig.10: Record 119 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

Fig.11: Record 203 at EP E AC =99% and EP E DC =50% threshold setting a.) Original signal b.) Reconstructed

signal c.) Error between (a) and (b).

Biomedical Engineering, IEEE Transactions on,

vol. 37, pp. 329-343, 1990.

[4] L. Zhitao, K. Dong Youn, and W. A. Pearlman,

“Wavelet compression of ECG signals by the

set partitioning in hierarchical trees algorithm,”

Biomedical Engineering, IEEE Transactions on,

vol. 47, pp. 849-856, 2000.

[5] M. Shaou-Gang, Y. Heng-Lin, and L. Chih-

Lung, “Wavelet-based ECG compression using

dynamic vector quantization with tree codevectors

in single codebook,” Biomedical Engineering,

IEEE Transactions on, vol. 49, pp. 671-680,

2002.

[6] P. S. Hamilton and W. J. Tompkins, “Compression

of the ambulatory ECG by average beat subtraction

and residual differencing,” Biomedical

Engineering, IEEE Transactions on, vol. 38, pp.

253-259, 1991.

[7] B. Huang and W. Kinsner, “ECG frame classification

using dynamic time warping,” in Electrical

and Computer Engineering, IEEE CCECE

2002, Canadian Conference on, 2002, pp. 1105-

1110 vol.2.

[8] C. Hsiao-Hsuan, C. Ying-Jui, S. Yu-Chien, and

K. Te-son, “An effective and efficient compression

algorithm for ECG signals with irregular

periods,” Biomedical Engineering, IEEE Transactions

on, vol. 53, pp. 1198-1205, 2006.


28 Y. Chompusri and S. Yimman: Energy Packing Efficiency Based Threshold Level Selection for DTW ... (19-28)

[9] D. Xiao-Li, G. Cheng-Kui, and W. Zheng-Ou,

“A Local Segmented Dynamic Time Warping

Distance Measure Algorithm for Time Series

Data Mining,” in Machine Learning and Cybernetics,

2006 International Conference on, 2006,

pp. 1247-1252.

[10] D. S. S. Lee, B. J. Lithgow, and R. E. Morrison,

“New fault diagnosis of circuit breakers,”

Power Delivery, IEEE Transactions on, vol. 18,

pp. 454-459, 2003.

[11] M. Abo-Zahhad and B. A. Rajoub, “An effective

coding technique for the compression of onedimensional

signals using wavelet transforms,”

Medical Engineering & Physics, vol. 24, pp. 185-

199, 2002.

[12] B. A. Rajoub, “An efficient coding algorithm

for the compression of ECG signals using

the wavelet transform,” Biomedical Engineering,

IEEE Transactions on, vol. 49, pp. 355-362,

2002.

[13] C. Hsiao-Hsuan, C. Ying-Jui, S. Yu-Chien, and

K. Te-Son, “A high performance compression

algorithm for ECG with irregular periods,” in

Biomedical Circuits and Systems, 2004 IEEE International

Workshop on, 2004, pp. S2/4-9-12.

Yotaka Chompusri was born in Thailand,

1976. She received B.Eng. degree

in Control Engineering from King

Mongkut’s Institute of Technology Ladkrabang,

Thailand in 1998 and received

M.Sc. degree in Electrical Engineering

from University of Southern

California, USA in 2002. She is

currently staff at Instrumentation and

Electronics Engineering Department of

King Mongkut’s University of Technology

North Bangkok, Thailand. Her research works in area of

control system, digital signal processing, data compression and

pattern recognition.

Surapun Yimman was born in Thailand,

1967. He received B.Ind.Tech. and

M.Eng. degree from King Mongkut’s

Institute of Technology North Bangkok,

Thailand in 1991 and in 1998, respectively

and received D.Eng. in Electrical

Engineering from King Mongkut’s Institute

of Technology Ladkrabang, Thailand

in 2007. He is currently staff at Industrial

Physics & Medical Instrumentation

Department of King Mongkut’s

University of Technology North Bangkok, Thailand. His research

interests in area of digital signal processing and application.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 29

No Visual Mismatch Negativity (MMN) for

Simultaneously Presented Audiovisual

Stimuli: Evidence from Human Brain

Processing

W. Sittiprapaporn, Member

ABSTRACT

The present study employed simultaneous audiovisual

stimuli in the oddball paradigm to re-examine

the effects of attention on audio, visual and audiovisual

perception. The study was designed to investigate

whether task-related processing of audio

and visual features was independent or task-related

processing in one modality might influence the processing

of the other. Electroencephalogram (EEG)

was recorded from 12 normal subjects. ANOVA

showed statistically significant of the interaction between

electrode site and modality. The difference

waves with 100-200 ms latency at the anterior sites

were markedly different to the posterior sites. The

emergence of posterior negativity in the audio-visual

modality might not be attributed to visual discrimination

process as it did not appear in the visual

modality. The findings reveal the processing of a feature,

hierarchically dependent on another feature in

the audiovisual perception.

Keywords: Event-related potentials, oddball

paradigm, Mismatch negativity, bisensory processing

1. INTRODUCTION

The human central auditory system has a remarkable

ability to establish memory traces for invariant

features in the acoustic environments such as music

and speech sounds in order to correct the interpretation

of natural acoustic sound heard. Even when no

conscious attention is paid to the surrounding sounds,

changes in their regularity can cause the listener to

redirect his or her attention toward the sound heard

[1]. Event-related potential (ERP) recordings have

bought new insight to the neuronal events behind

auditory change detection in audition. ERPs components

(see Fig. 1) reflect the conscious detection

of a physical, semantic, or syntactic deviation from

Manuscript received September 30, 2009; revised on December

28, 2009.

W. Sittiprapaporn is with College of Music, Mahasarakham

University, Khamriang, Kantarawichai, Mahasarakham 44150

Thailand.

E-mail addresses: drwichain@hotmail.com (W. Sittiprapaporn)

the expected sounds [1]. The ERP recordings thus

allow one to probe the neural processes preceding the

involvement of the attentional mechanisms. For instances,

ERPs have been recorded that reflect memory

traces representing sounds composed of several

simultaneous or successive tonal elements [2-4].

Fig.1: Event-Related Potential (ERP) Components.

Mismatch negativity (MMN) component of ERP is

theoretically elicited in the auditory cortex when incoming

sounds are detected as deviating from a neural

representation of acoustic regularities (see Fig. 2).

It is mainly generated in the auditory cortex [5] occurring

between 100 to 250 ms and thus long been

regarded as specific to the auditory modality [6-7].

The automatic change-detection system in the human

brain as reflected by the MMN requires the storage

of the previous state of the acoustic environment for

detecting an incoming deviating sound [6,8]. MMN

implies the existence of an auditory sensory memory

that stores a neural representation of a standard

against which any incoming auditory input is compared

[9]. In the auditory modality, MMN is an automatic

process which occurs even when the subject’s

attention is focused away from the evoking stimuli [6].

Its onset normally begins before the N2b-P3 complex

which occurs when attention is directed to the stimuli.

The duration of MMN varies with the nature of


30 W. Sittiprapaporn: No Visual Mismatch Negativity (MMN) for Simultaneously Presented Audiovisual Stimuli ... (29-35)

the stimulus deviance but it invariably overlaps N2b

when the latter is present [10].

Fig.2:

ERP.

Mismatch Negativity (MMN) Component of

Previous study [11] has stated that the automatic

detection of stimulus change plays a part in directing

attention to events of biological importance. If

this is the case, one would expect a similar mechanism

to operate in the visual modality. Although

it is clear that the MMN can be elicited in auditory

modality in the absence of attention, it remains somewhat

unclear whether there is an analogous automatic

deviant-related negativity (DRN) elicited outside the

auditory modality. Näätänen [12] has stated that

the automatic detection of stimulus change plays a

part in directing attention to events of biological importance.

If this is the case, one would expect a

similar mechanism to operate in the visual modality.

Even though MMN had not mentioned to be

appeared in the visual modality [12], several studies

have shown that visual stimuli deviating from repetitive

visual standards can also elicit a visual analogue

of the MMN in the same latency range. This visual

MMN seems to be mainly generated in occipital areas

[12,13] with possibly a more anterior positive component

[14,15]. In addition, Cammann’s study [16]

showed a widely distributed MMN change between

150 and 350 ms, with a parietal maximum suggesting

that this MMN may occur in the visual modality.

Recently, Pazo-Alvarez et al. [17] reviewed several

previous reports to provide convincing evidence for

the existence of this visual MMN. Moreover, crossmodal

attention studies clearly showed that deviant

visual stimuli elicited MMN, largest over the inferior

temporal cortex. This visual MMN increased in amplitude

with attention, but it was also evident during

inattention [18,19].

In the present study, simultaneous audio-visual

stimulus in the oddball paradigm was used to reexamine

the effects of attention on MMN in auditory,

visual and audiovisual dimensions. Attentional

ERP components were analyzed in a situation where

target stimuli were combinations of both auditory

and visual features. Interactive processing of stimulus

features would then be indicated by the absence,

reduction or early termination of the attentionrelated

components [20] as a function of processing of

the other feature. If visual-specific components are

evoked by visual deviances, then the present audiovisual

paradigm will help to separate them from the

effect of visual information on the auditory-specific

MMN process by facilitating the focus of attention on

auditory and visual MMNs elicited with bimodal features.

The audio-visual paradigm was also designed

to investigate whether task-related processing of visual

and auditory features was independent or taskrelated

processing in one modality might influence the

processing of the other.

2. MATERIALS AND METHODS

2.1 Subjects

Twelve right-handed normal subjects (6 males and

6 females) with a mean age of 24.83 (SD= 3.54) participated

in the experiment. All participants had normal

hearing and had been corrected to normal vision

(self reported). None of them had more than three

years of formal musical training and none had any

musical training within the past five years. All participants

had no history of neurological or psychiatric

history. After a complete description of the intended

study, written informed consent was obtained. The

subjects were paid for their participation.

2.2 Stimuli

Stimuli consisted of a set of four audio-visual stimuli

that were distinguished by frequencies (Hz) for audio

and features for visual appearing on the screen.

The duration of the stimuli were 300 ms. The stimulus

system (STIM, Neurosoft, Inc. Sterling, USA)

was employed for controlling the presentation of the

stimuli. An oddball paradigm [1] was chosen for presenting

randomized stimulus sequences consisting of

all four sets of equiprobable audio-visual stimuli (a simultaneous

combination of audio and visual stimuli):

the deviant was ’X’ with 1800 Hz tone (Visual Target

Audio Target; hereafter, VTAT) in 10% probability,

and the standard was ’Y’ with 800 Hz tone (Visual

Non-target Audio Non-target; hereafter, VNAN) in

70% probability were presented as preferred-deviant

to be able to check that participants were attending

the stimuli. Additionally, the deviant ’X’ with

800 Hz tone (Visual Target Audio Non-target; hereafter,

VTAN) and the deviant ’Y’ with 1800 Hz tone

(Visual Non-target Audio Target; hereafter, VNAT)

were used in 10% probabilities (see Fig. 3). While visual

stimuli were presented on the computer screen,

acoustic/audio stimuli were delivered binaurally to


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 31

the participants through plastic tubes and earpieces.

Sound density was adjusted to be 85 dB above the

participant’s hearing threshold.

30 Hz. The averaging epoch was 900 ms, and the 100

ms before the onset of the presenting stimuli served

as baseline. The artifact-free epochs were filtered at

0.1-15 Hz, baseline corrected and averaged. The artifacts

rejection was conducted in all channels with

threshold of ± 100 µV before averaging. Epochs with

EEG or EOG with a large (>100 µV) amplitude were

also automatically rejected. The limitation restricted

EEG recording time to 90 mins, thus minimizing the

risk of participants’ fatigue (see Fig. 4).

Fig.3: Schematic presentation of the stimuli in

bisensory conditions. Stimuli in different modalities

are presented simultaneously. The VTAT and VNAN

conditions appeared in 10% and 70% probabilities, respectively.

Additionally, the VTAN and VNAT conditions

were equally presented in 10% probabilities.

The stimulus is presented with 300 ms duration, and

inter-stimulus interval is 1,800 ms in every condition.

2.3 Procedures

The experiment consisted of 3 blocks and each

block had 300 trials. Every stimulus was presented

with 300 ms exposure duration and inter-stimulus interval

was 1,800 ms (from audio/visual stimuli onset

to onset) in every condition. Subjects sat in an electrically

shield and soundproofed room with the response

buttons under their hands. The subjects had

to press the button on the response pad when the deviant

(VTAT) was presented and ignored any other

types of stimuli. Prior to the experimental session, a

practice block was administrated to ensure that the

subjects understood the task.

2.4 Electroencephalogram recording

Electroencephalographic (EEG) data were collected

in an electrically and acoustically shielded

room. EEG was recorded from a Quick-Cap equipped

with 128 channels according to the international 10-

20 system using Scan system (Scan 4.2, Neurosoft,

Inc. Sterling, USA). Linked mastoids were used as

reference. Eye movements were monitored with two

EOG electrodes. Four electrodes monitored horizontal

and vertical eye movements for off-line artifact

rejection. Vertical and horizontal electro-oculogram

(EOG) was recorded by electrodes situated above and

below the left eye, and on the outer canthi of both

eyes, respectively. Impedance was maintained at 5kΩ

or less. During the experiment, EEG was amplified

with a bandpass of 0.05 - 100 Hz, sampled at 1,000 Hz

and stored on a hard disk for off-line analysis. ERPs

were averaged separately for each types of stimulus.

They were digitally filtered with a bandpass of 0.1 -

Fig.4:

Electroencephalogram (EEG) Recording.

2.5 EEG data analysis

After the data recordings, the EEG was segmented

into 1000 ms epochs, including the 100 ms prestimulus

period. The baseline was corrected separately

for each channel according to the mean amplitude

of the EEG over the 100 ms period that preceded

stimulus onset. The EEG epochs contained

amplitudes exceeding ±100 µV at any EEG channels

were automatically excluded from the averaging.

The epoch was separately averaged for the standard

and deviant stimulus. The average waveforms obtained

from the standard and deviant stimuli were

digitally filtered by a 0.1 - 15 Hz band-pass filter and

finally baseline-corrected. To analyze the deviantrelated

components, difference potentials were calculated

where responses elicited by the VNAN stimuli

were subtracted from responses to VTAN and VNAT

stimuli after stimulus onset referred to visual (Vi)

modality as in (1) and auditory (Au) modality as in

(2), respectively.

and

(V NAN) − (V T AN) = (V i) (1)

(V NAN) − (V NAT ) = (Au) (2)

In the audio-visual (AV) modality, VTAT minus

VNAN difference was also calculated as in (3).


32 W. Sittiprapaporn: No Visual Mismatch Negativity (MMN) for Simultaneously Presented Audiovisual Stimuli ... (29-35)

(V NAN) − (V T AT ) = (AV ) (3)

The amplitude of the difference waveform was expressed

in microvolt and its latency in milliseconds.

MMNs were statistically assessed by two-tailed t-tests

comparing the averaged amplitude of the deviant minus

standard difference waveform to zero in the 40

ms time-window around the latency of the peak in

the grand-average responses. To compare these components,

MMN amplitudes were further assessed via

two-way analyses of variance (ANOVA) with repeated

measurements. The factors were modality (three levels:

Vi, Au and AV), and electrode site (two levels:

anterior sites at F3, Fz, F4, C3, Cz, C4, and posterior

sites at P3, Pz, P4, O1, Oz, O2).

3. RESULTS

Reaction times and response accuracy (mean and

standard deviation: SD) are shown in Table 1. Figure

5 presents the grand-average deviant-related components

in the Au, Vi and AV modalities producing

deviant-related negativities (DRNs). DRNs were divided

into an early DRN1 around 100-200 ms and

a late DRN2 around 200-300 ms. According to the

previous studies showing that MMN appears between

100 to 250 ms [6] and the characteristics of DRN2

match with those of N2b component [23]. The present

study thus associated DRN1 mainly with MMN in

which we focus in this report, and DRN2 with a mixed

wave of MMN and N2b.

way repeated measures ANOVA shows that the

interaction between electrode site and modality of

MMN amplitudes at 100-200 ms of all modalities was

statistically significant [F(11,429) = 8.27, p ¡ 0.0001].

At 200-300 ms, significant levels were also reached in

the same interaction for N2b component [F(11,429)

= 6.50, p ¡ 0.0001]. As shown in Fig. 2, the difference

waves with 100-200 ms latency at the anterior

sites were markedly different to the posterior sites.

Additionally, there was no MMN elicitation for the

Vi modality at the posterior sites compared to the

Au and AV modalities. We thus compared MMN

mean amplitudes of all modalities. Two-way repeated

measures ANOVA shows that the interaction between

posterior electrode site and modality was statistically

significant [F(17,663) = 27.52, p ¡ 0.0001] and significant

level was also reached in the interactions between

anterior electrode site and modality [F(17,663)

= 52.37, p ¡ 0.0001]. We then compared the MMN

mean amplitude values of Au, Vi and AV difference

potentials at Fz site. The difference was statistically

significant [F(2,78) = 8.75, p ¡ 0.0001]. Like

the MMN, they showed similar significant effect on

the N2b amplitude at Oz site [F(2,78) = 6.50, p ¡

0.0001].

The additivity of the MMN was also examined by

adding together the Au and Vi MMNs and comparing

this ’modelled’ (AuVi) MMN with the AV MMN

in order to see the possible attention effects on the

additivity of MMN. If processing of Au and Vi is independent

of the others, the sum of the MMNs to

both modalities should be equal to the MMN elicited

by the AV modality. We found that the additivity of

Au and Vi MMNs existed in both anterior and posterior

locations. However, the additivity of Au and Vi

MMNs amplitude was slightly larger than that of the

corresponding AV modality, being maximum at P3

(mean amplitude; AuVi vs. AV: -1.56 (0.02) µV vs.

-1.12 (0.02) µV, t(39) = -21.89, p < 0.0001). Moreover,

the N2b component, following MMN, was larger

than that of the AV modality, being maximum at Fz

(mean amplitude: -4.51 (0.04) µV vs. -3.97 (0.05)

µV, t(39) = -45.99, p < 0.0001). The N2b was also

followed by a positive component identified as P3a [1]

(see Fig. 6).

4. DISCUSSION

The main finding of our study indicates that the

prominent response to the Au, Vi and AV modalities

produces deviant-related negativities. DRNs were divided

into early DRN1 (or MMN), and late DRN2

(or N2b). As shown in Fig. 5, the difference waves

with 100-200 ms latency at the anterior sites were

markedly different to the posterior sites. There was

no MMN elicitation for the visual modality at the

posterior electrode sites compared to the auditory

and audiovisual modalities. The MMN was significantly

larger only in the anterior sites, being maximum

at F3 (t(39) = -68.04, p ¡ 0.0001). This result

was consistent with a previous study showing no posterior

negativity elicitation in the difficult discrimination

task [22]. Moreover, the present result extends

previous findings [23,25] which showed that the

deviance related ERP effects in vision could be separated

from automatic processing of other stimulus

features. We hypothesize that the emergence of posterior

negativity (MMN) in the present study is not

to be attributed to visual discrimination process. Our

result supports the view proposed by Näätänen [12]

that “no MMN appears to occur in the visual modality”.

However, several studies have shown that visual

stimuli deviating from repetitive visual standards can

also elicit a visual analogue of the MMN in the same

latency range [12,24,14,15].

Furthermore, cross-modal attention studies showed

that deviant visual stimuli elicited MMN, largest

over the occipital and inferior temporal cortex [19].

This visual MMN was not affected by the processing

load during attention to the other modality and had

restricted, occipito-temporal distribution, consistent

with generation in modality-specific sensory cortex.

This early MMN-like portion of the visual deviancerelated

negativity was independent of attention. It

increased in amplitude with attention, but it was also

evident during inattention [19]. However, Alho et al.

[25] has argued that if a visual MMN exists, its elicita-


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 33

tion may have a higher threshold than auditory MMN

which evoked by any discriminable change. The effect

of target-specific negativity is thus a considerable

contamination factors in which the impact of simultaneous

memory traces in different modalities could

also be considered. In addition, the persistence of the

visual MMN may reflect the automatic detection of

physical change in sequences of visual stimuli [12].

As shown in Fig. 5, the identical N2b components

were elicited by Vi and AV modalities, whereas in case

of Au modality, latency of this component was longer

than that of the Vi and AV modalities. The N2b effect

suggests the attention-related rechecking of the

outcome of within-modality analyses. Such activity

would have been different upon the different discrimination

demand [20,22]. Therefore, the process underlying

N2b component thus performs independent

within-dimension selection [20]. The shorter N2b latency

to AV further suggests that this component is

a correlate of processes following the elementary discrimination

processes, instead of being an on-line correlate

of such processes [22].

The additivity of the MMN was also examined by

adding together the Au and Vi MMNs and comparing

this ’modelled’ (AuVi) MMN with the AV MMN in

order to see the possible attention effects on the additivity

of MMN. Assuming that the MMNs to these

features are generated by different, non-interacting

neural populations, each deviating feature in the bimodal

deviants should elicit its own MMN. The additivity

of Au and Vi deviants should thus elicit a

larger MMN than the AV deviant. As shown in Fig.

6, our findings show that the additivity of the Au

and Vi MMNs was larger than that of the AV modality.

This implies that there are complex interactions

between brains processes involved in analyzing several

simultaneous deviant features in the AV modality.

Our results are in the line of previous studies

revealing that at least partly different neural populations

are involved in processing deviance in different

auditory features and being suggest the independent

MMN generators for these features [26-29]. Moreover,

the underadditivity of AV MMN in the present study

suggests either that common neural populations are

involved in the controlled processing of changes in different

features [27] or that the populations are separate

but strongly interacting [29]. However, Paavilainen

et al [29] has argued that the additivity hypothesis

of MMN does not hold at least in its simplest

form which presupposes that the processing of various

features is completely independent of each other,

the contributions of the different simultaneous MMNs

just simply ’piling up’ in the ERPs. According to this

hypothesis, the AuVi MMN in the present study was

slightly larger than did the corresponding AV modality,

being maximum at P3 site. Like AuVi MMN, the

N2b component was also larger than that of the AV

modality, being maximum at Fz site.

, our findings are consistent with the previous

study showing that the deviant-related negativities

consist of two successive components, the earlier being

generated at the auditory cortex and the latter at

the frontal areas [30]. In addition, the degree of additivity

may be different from those two components.

That is, the existence of several partially overlapping

and interacting brain processes may complicate the

estimation of the additivity of MMN [29]. The negativity

associated with deviants in the present study

thus resembles auditory mismatch negativity inasmuch

as it occurs automatically while the focus of

attention is directed away from the evoking stimuli.

Finally, the morphology of ERPs to AuVi modality

differed to the AV modality. The N2b was followed

by a large positive component identified as P3a [6]

in the AuVi modality, but little P3a was evident in

the AV modality (see Fig. 6). Like the MMN/N2b,

the empirical AuVi P3a tended to be larger than the

AV P3a. Its amplitude increased as the number of

the deviant features was increased, with the additivity

of Au and Vi deviants eliciting a larger P3a

than did the corresponding AV deviants. This implies

that both Au and Vi modalities were demanding,

performance in the inattention remained high,

and the occurrence of P3a component depend on another

feature. The occurrence of AV P3a also implied

the complex interactions between brains processes

involved in analyzing several simultaneous deviant

features. This component possibly reflects involuntary

attention-switching mechanism to deviant

stimuli [23,31,32]. Consequently, our results support

the view that the processing of a feature, hierarchically

dependents on another feature [20,22].

5. CONCLUSION

The present study demonstrates the audiovisual

interaction following elementary within-modality discrimination

processes. MMN and N2b effects suggest

the attention-related rechecking of the outcome

of within-modality analyses. The task-related processing

of audio and visual features was independent

and one modality might influence the processing of

the other. This findings support the view that the

processing of a feature, hierarchically dependent on

another feature in the condition of audio-visual perception.

6. ACKNOWLEDGMENT

This research was conducted in cooperation with

Clinical Cognitive Neuroscience Center (CCNC) in

the Seoul National University, College of Medicine,

Seoul, Korea. The author gratefully thank Prof. Dr.

Jun Soo Kwon, Dr. Do-Hyung Kang, Dr. Kyung

Whun Kang and Dr. Bo Reom Lee for their guidance

and providing the equipment and data sources during

this work. In addition, this research was supported by


34 W. Sittiprapaporn: No Visual Mismatch Negativity (MMN) for Simultaneously Presented Audiovisual Stimuli ... (29-35)

the following organizations; the Brain Research Center

of the 21st Century Frontier Research Program

(Ministry of Science and Technology of Republic of

Korea), and the International Scholar Exchange Fellowship

(ISEF) Program, 2006-2007 (Korea Foundation

for Advanced Studies).

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1+1=2 but 1+1+1


36 A. Leelasantitham and S. Kiattisin: A Diagnosis of Tonsillitis using Image Processing and Neural Network (36-42)

A Diagnosis of Tonsillitis using Image

Processing and Neural Network

A. Leelasantitham and S. Kiattisin, Members

ABSTRACT

Tonsillitis is a disease occurring mostly in child and

adults as this disease may take to the other effects.

Nowadays, a detection of tonsil grand exploits medical

doctor’s diagnosis to check on oral cavity. Therefore,

this paper presents a diagnosis of tonsillitis using

image processing and neural network (NN). There are

three steps described as follows. The first step is localization

of tonsil grand (TG) using Ellipses Hough

Transform. The second step is feature extraction using

three important factors which can be indicated in

swelling by images of TG in terms of a) a dimensional

ratio of TG, b) an average intensity of TG and c) a

purulent surface of TG (yes/no) using power spectrum

of two dimensional Fast Fourier Transform (2D

FFT). The final step is verification using NN. The

three factors are inputted into NN, and TG samples

of 50 images are used for training into the NN. They

are divided by tonsillitis patience 25 images and usual

TG of 25 images. This experiment uses 100 images

of TG for testing NN. Therefore, the overall accuracy

is at approximately 90% in terms of comparing with

the results from the medical doctor.

Keywords: tonsillitis; diagnosis; neural network;

image processing; 2D FFT,Ellipses Hough Transform

1. INTRODUCTION

Tonsil grand (TG) is the group of tissue type as

lymphatic gland which is placed inside of buccal cavity

called ‘Palatine Tonsil’. The inflamed disease of

tonsil is a swell of tonsil grand (like a little of tubercules)

because of the mostly causes from infectiousness

such as virus and bacteria. Tonsillitis is not a

serious disease but the disadvantage of the inflamed

TG will show its area in an oral cavity. If it has

occurred both of inflammable and growing TG to a

big grand, then a respiratory pathway of human will

be clogged in their breath or else this infection may

severely become a ulcer in throat. Infectious inci-

Manuscript received on September 30, 2009; revised on December

28, 2009

Adisorn Leelasantitham and Supaporn Kiattisin are from

Computer and Multimedia Engineering, School of Engineering,

University of the Thai Chamber of Commerce 126/1,

Vibhavadee-Rangsit Road, Dindaeng, Bangkok 10400, Thailand

E-mail addresses: adisorn lee@utcc.ac.th (A. Leelasantitham)

supaporn kai@utcc.ac.th (S. Kiattisin)

dence is occurred in the blood and it may affect, for

example, the heated level of middle ear, the sinusitis,

the bronchitis or the pneumonia. Such diseases are

important problems in the tonsillitis of child in the

case of the inflammation or the severe heart occurred

from the infectious streptococcus. If it takes time to

be chronic, then it may cause a valve disease with

disabilities and heart failure [1].

Diagnosis of doctor will check the oral cavity to see

any features of TG which is exposed for questioning

symptoms of history from the patience. For diagnosis

in oral cavity, the doctors will see directly the oral

cavity to observe the features of TG, the disease can

be intercommunication, and the patience has drawn

a breath odor. An alternative method to help the

medical doctor for diagnosis of the tonsil is the use of

images and computer because the medical doctor will

check the oral cavity to see closely the tonsil grand of

the patience. As a result, he sometimes may receive

the disease from the patience. To support the practical

doctor, it has taken pictures in the oral cavity to

see a feature of tonsil grand for instead of checking

directly in the oral cavity. In open literature, only

three related papers have involved with finding out

the tonsil using images [2], [3]. Such two papers have

checked two factors as follows: 1) a size of TG using

the sized ratio of a usual TG (from the previous

diagnosis) and a new TG (from the present diagnosis);

as well as, 2) an average intensity of the area

of TG. These two factors will be sent to fuzzy logic

to make a decision. However the information of such

two papers is not sufficient to analyze for checking

tonsillitis because of only two factors (i.e. the size

and intensity).

In this paper, a diagnosis of tonsillitis using image

processing and neural network (NN). There are three

steps described as follows. The first step is localization

of tonsil grand (TG) using the Ellipses Hough

Transform. The second step is feature extraction using

three important factors which can be indicated in

swelling by images of TG in terms of a) a dimensional

ratio of TG, b) an average intensity of TG and c) a

purulent surface of TG (yes/no) using power spectrum

of two dimensional Fast Fourier Transform (2D

FFT). The final step is verification using NN. The

three factors are inputted into NN, and TG samples

of 50 images are used for training into the NN. They

are divided by tonsillitis patience 25 images and usual

TG of 25 images. This experiment uses 100 images

of TG for testing NN. Therefore, the overall accuracy


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 37

is at approximately 90% in terms of comparing with

the results from the medical doctor.

2. FEATURES OF TONSIL GRANDS

Figure 1 shows tonsil grand (TG) called “Palatine

Tonsil” to be two lymph nodes located in the oral

cavity on the side of neck with nearly tongue baseness

where we can see it when we open a mount.

Figure 2 shows the tonsillitis which the oral cavity

can be found in the grown dimension and the timidly

inflammable red. If it reaches severe inflammation,

then it will be seen in translucent whites covering

TG called as “ulcer” [4], [5].

A system of the detection of tonsillitis im-

Fig.3:

ages.

Fig.1: The usual tonsil grand [5].

Fig.4: Flowchart for detection of tonsillitis images

based on medical knowledge and neural network.

3.1 Localization

Fig.2: Tonsillitis called as “ulcer” [5].

3. PROPOSED METHODOLOGY

Figure 3 shows a system of the detection of tonsillitis

images which the system consists of a tonsil

grand (TG), a camera and a computer. The process

is to start the camera capturing the TG and sending

it to the computer for processing three factors i.e. 1)

the dimensional ratio of TG, 2) the average intensity

of TG and 3) the purulent surface of TG.

Figure 4 shows a flowchart of the detection of tonsillitis

images based on medical knowledge and neural

network. There are three main steps of the proposed

methodology i.e. the localization, the feature extraction

and the verification. The details of the three

main steps can be described as follows.

This paper uses the Ellipse Hough Transform

(EHT) to find the edge of TG because the shape of

TG is nearly related in ellipse shape. Therefore, the

equation of the EHT can be defined as [6]

(u·cos θ+v·sin θ−x) 2

a 2

+

(−u·sin θ+v·cos θ−y)2

b 2 = 1

where u and v are image point,

H(x,y,a,b,θ) is a hough space,

x, y, a, b and θ are parameters for the EHT.

(1)

Figure 5 shows an example for localizing the edge

of TG. The process is started from a TG image converted

to a binary image. As a result of the binary

image of TG, the position of TG is located by the

EHT. As shown in Figure 5, the dotted line of TG

edge is generated by equation (1) until it finds out

(0 or 1) all possible parameters (x,y,a,b,θ) that could

pass (u,v) vote for parameters (x,y,a,b,θ), then the

solid line can cover the final edge of TG.


38 A. Leelasantitham and S. Kiattisin: A Diagnosis of Tonsillitis using Image Processing and Neural Network (36-42)

Fig.6: An example for finding the dimensional ratio

of TG.

3.2.2 The average intensity of TG

Fig.5:

An example for localizing the edge of TG.

3.2 Feature Extraction

3.2.1 The dimensional ratio of TG

The first factor is a result of the TG dimensional

ratio which can be found by measuring a size of TG

(ellipse shape) which is the width (W) and height (H).

The position of TG shape is obtained from the EHT.

Therefore, the equation is equal to

R = W H

(2)

which R is the dimensional ratio of width and

height,

W is the width of TG (pixels),

H is the height of TG (pixels).

Figure 6 shows an example for finding the dimensional

ratio of TG. The values (pixels) of W and H

are obtained from the binary image which the shape

of TG is related to the ellipse shape shown in Figure

6. Therefore, the result of the dimensional ratio of

TG is calculated by equation (2). Note that the W

and H are the minor and major axes, respectively, of

ellipse shape.

The second factor is a result of the TG average

intensity resulted from three colors (red, green and

blue [R, G, B]) which are not the same values. R

values are normally higher than G and B values in the

TG image. However, there are some images which G

or B values are nearly to R values. All R, G and B

are important to use this method for verifying the TG

image. For another reason, the TG generally is a high

redness then it may be the tonsillitis, nevertheless it

sometimes may not be the tonsillitis. Moreover, there

are two factors i.e. the first and third factors being

necessary to consider in this case. Such two factors

will also be utilized for classification of the TG image

(the normal TG and tonsillitis) using neural network

(NN) which will be described further in Section 3.3.

In addition, there are usually many methods for

classification of the normal TG and tonsillitis such

as histogram intersection or Euclidean distance etc.

However, there are limitations of such methods for

classifying the TG image because they will classify

only the data group in a linear model. In this work,

the scattering plot is not clear for a separation of

two data groups; however, some data also have overlapped

between the normal and tonsillitis, as will be

seen later in Section 3.3. As a result, it is difficult

to classify both groups for a correction of the results.

Therefore, this work proposes to use neural network

for classification of the TG image i.e. the normal TG

and tonsillitis.

The average intensity of TG exploiting the intensity

of R, G and B in the TG area can be described

as the formula

A =

n∑ ∑

r + n ∑

g + n b

i=1

i=1

3n

i=1

(3)


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 39

where A is the average intensity of TG area,

r is the intensity of red,

g is the intensity of green,

b is the intensity of blue,

n is the total pixels of the TG area.

Then, the average intensity (A) of TG area is normalized

by 255 for a suitable value as following equation

N =

A

255

where N is the normalized value of A (0 to 1).

(4)

Figure 7 shows an example for displaying the values

of intensity (red, green and blue) on the area of

TG. For example, it can be seen from Figure 7 that

the values of a pixel (in case of n =1) selected from

the TG area consist of r = 198, g = 118 and b = 69.

Therefore, the average intensity (A) using equation

(3) is equal to (198+118+69)/3 = 128.33. In case of

n 1, the average intensity (A) is calculated from the

TG area for n 1 and, the summations of r, g, and b

are obtained from n 1. Finally, the average intensity

(A) will be normalized by 255 for the suitable value,

as shown in equation (4).

be an inflammation of the TG. Therefore, the 2D FFT

and power spectrum of 2D for calculating the result

of the surface on the TG will be defined as follows [7]

F (u, v) = 1

MN

M−1


x=0

N−1


y=0

f(x, y)e −j2π(ux/M+vy/N) (5)

where F(u,v) is the 2D FFT result of picture,

N,M are width and height of picture,

f(x,y) is brightness value at (x,y).

The result from the equation (5) is taken to calculate

the power spectrum of 2D as the equation

P (u, v) = |F (u, v)| 2 = R 2 (u, v) + I 2 (u, v) (6)

where P is the power spectrum of 2D,

R is real part,

I is imaginary part.

Figure 8 shows an example of the resulting image

using the 2D FFT on the area of TG. It can be seen

from Figure 8 that the binary image of the TG area

is applied for equation (5). Therefore, the 2D FFT

image of the TG area is shown in Figure 8. Figure 9

shows a resulted graph derived from Figure 8 around

the TG area using the 2D FFT. It can be seen from

Figure 9 that the calculated value of power spectrum

of 2D FFT image is at approximately 9.05 × 10 4 i.e.

it will be the purulent surface on the TG (or tonsillitis).

Note that the power spectrum (P) of 2D FFT

is derived from the resulting values in equation (5)

substituted in equation (6).

Fig.7: An example for displaying the values of intensity

(red, green and blue) on the area of TG.

3.2.3 The purulent surface on TG

The third factor is to calculate a result of a surface

on TG using the power spectrum of 2 dimensional

Fast Fourier Transform (2D FFT). For example, if the

calculated result reveals smooth surface on the TG,

then it may be a normal TG whilst if the calculated

result shows purulent surface on the TG, then it may

Fig.8: An example of the resulting image using the

2D FFT on the area of TG.


40 A. Leelasantitham and S. Kiattisin: A Diagnosis of Tonsillitis using Image Processing and Neural Network (36-42)

Fig.9: An example of a graph for the power spectrum

of the 2D FFT image around the TG area.

Fig.10:

factors.

The scattering plot of the three important

3.3 Verification

3.3.1 Data Analysing

This section describes an analysis of the data consisting

of three important factors based on medical

knowledge. Such three important factors are as follows:

a) the dimensional ratio of TG,

b) the average intensity of TG and

c) the purulent surface of TG.

The values of these three factors are plotted on

three axes consisting of the width/height (W/H), the

average intensity and the purulent surface for the

first, second and third axes, respectively. The plotted

values of the W/H, the average intensity and the purulent

surface are derived from the equations (2), (4)

and (6), respectively. Therefore, the scattering plot

of the three important factors will be shown in Figure

10. As shown in Figure 10, symbols of ‘•’ and ‘×’ are

represented to the usual TG and tonsillitis, respectively.

It can be seen from Figure 10 that the data

groups of ‘•’ and ‘×’ cannot be classified clearly in

terms of a simple method of linear analysis because

there is some data overlapped between two groups.

Therefore, neural network is an alternative method

for solving this classification which will be described

further in Section 3.3.2.

3.3.2 Neural Network

This section designs neural network (NN) using a

WEKA package [8] which there are 3 layers i.e. input,

hidden and output layers, as shown in Figure

11. Numbers of nodes consist of 3, 2 and 2 nodes

for the input, hidden and ouput layers, respectively.

It can be seen from Figure 11 that the three factors

(the W/H, the average intensity and the purulent surface)

are inputted to the input layer whilst there are 2

nodes for the output layer consisting of the usual TG

(value = 0) and tonsillitis (value = 1). The equation

for calculation of backpropagation neural network can

be defined as [9]

∆W ij = ηδ i x j + α∆W ij (k − 1) (7)

which W ij is weight to connect between output

node j and input node i

η is step size

α is coefficiency of momentum

x j is input signal from node j

δ i is the quantity of difference of error , δ i can be

shown as below

for output unit

otherwise

δ i = {O ′ i(t i − O i )

O ′ i is derivative of O i

T i is a required signal to node i

O i the actual output at node i.

Fig.11:

O ′ i


Wmi δ m (8)

Designing three layers of neural network.

4. EXPERIMENTAL RESULTS AND DISS-

CUSIONS

This section will verify the proposed method for

the detection of tonsillitis images based on medical


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 41

knowledge and neural network (NN). This paper uses

a MATLAB program for implementation of the first

part of image processing and the second part of NN

using a WEKA package [8]. Details of preparing the

neural network can be described as follows:

a) To demonstrate data inputs for training of neural

network, there are 25 images of tonsillitis whilst

there are 25 images of the usual TG.

b) To demonstrate data inputs for testing the detection

of tonsillitis, there are 100 images of TG. They

are taken into NN.

Table 1 shows the testing results of verifying TG

images which the output results are the usual TG or

tonsillitis. It can be seen in Table 1 that the 100

images of TG are tested by both of the medical doctor

and the proposed method. As shown in Table

I, the results from the medical doctor can diagnosis

the following outputs i.e. 40 and 60 images for the

usual TG and tonsillitis, respectively; whilst the results

from the proposed method can classify the following

outputs i.e. 35 and 55 images for usual TG

and tonsillitis, respectively. There is an error image

of them for the usual TG which their accuracy is at

approximately 87.5%, whilst; there is also an error

image of them for the tonsillitis which their accuracy

is at approximately 91.67%. Therefore, the overall

accuracy is at approximately 90%.

Table 2 shows summary results of the true or false

positive, and the true or false negative. It can be seen

in Table 2 that there are 35 images for the true positive

being the usual TG whilst there are five images

for the false positive being the tonsillitis. As shown

in Table 2, there are 55 images for the true negative

being the tonsillitis whilst there are five images for

the false negative being the usual TG. From the experimental

results, the correct results depend on clear

images, and NN is considered in terms of numbers of

training set. If there are more samples of training set,

the new results will chance an increment from the old

results.

Table 1: The testing results of verifying TG images.

Output

Results

Correct

Results

Correct

Results

Accuracy

(%)

of TG From From The

Images Medical

Doctor

Proposed

Method

Usual TG 40 35 87.5

Tonsillitis 60 55 91.67

Table 2: Summary results of the true or false positive,

and the true or false negative.

True False

Positive 35 5

Negative 55 5

5. CONCLUSION

This paper has presented the diagnosis of tonsillitis

using image processing and neural network (NN).

There are three steps described as follows. The first

step is localization of tonsil grand (TG) using the Ellipses

Hough Transform. The second step is feature

extraction using three important factors which can

be indicated in swelling by images of TG in terms of

a) a dimensional ratio of TG, b) an average intensity

of TG and c) a purulent surface of TG (yes/no) using

the power spectrum of two dimensional Fast Fourier

Transform (2D FFT). The final step is verification using

NN. The three factors are inputted into NN, and

TG samples of 50 images are used for training into

the NN. They are divided by tonsillitis patience 25

images and usual TG of 25 images. This experiment

uses 100 images of TG for testing NN. Therefore, the

overall accuracy is at approximately 90% in terms of

comparing with the results from the medical doctor.

6. ACKNOWLEDGMENT

The authors are grateful to Mr. Kritchanon Jirawanitcharoen

for his useful help in this work.

References

[1] Medical Doctor Worawut Chareonsiri:

http://www.bangkokhealth.com

[2] P. Phensadsaeng, P. Kumhom, and K. Chamnongthai,

2005, “A Method of Vision-Based Tonsillitis

Detection”, Sixth International Conference

on Intelligent Technologies (InTech’05), 14-

16 December 2005, Phuket, pp. 789-792.

[3] P. Phensadsaeng, P. Kumhom, and K. Chamnongthai,

2006, “A Computer-aided-Diagnosis of

Tonsillitis Using Tonsil size and color”, International

Symposium on Circuits and System (IS-

CAS2006), Greece, May 21-24, 2006, pp. 5563-

5566.

[4] Medical Doctor Panuwit Pumhirun:

http://www.vichaiyut.co.th

[5] K. Jirawanitcharoen, S. Kiattisin, A. Leelasantitham

and P. Chaiprapa, “A Method of Detecting

Tonsillitis Images Based on Medical Knowledge

and Neural Network”, The 2009 International

Conference on Artificial Intelligence

and Neural Networks (ICAINN 2009), Beijing,

China, August 8-11, 2009.

[6] X. Yu, H.W. Leong, C. Xu and Q. Tian, “A robust

and accumulator-free ellipse hough transform”,

Proceedings of the 12th ACM International

Conference on Multimedia (ACM Multimedia

2004), New York, USA, October 10-16,

2004, pp. 256-259.

[7] C. Gonzalez, Rafael and E. Woods, Ricard, Digital

Image Processing 2nd , Prentice Hall, 2002.

[8] I. H. Witten and E. Frank, Data mining: Practi-


42 A. Leelasantitham and S. Kiattisin: A Diagnosis of Tonsillitis using Image Processing and Neural Network (36-42)

cal machine learning tools and techniques (2 ed.),

Morgan Kaufmann, San Francisco, CA, 2005.

[9] Dan W. Patterson, Artificial Neural Networks

Theory and Application, Prentice Hall, 1996.

Adisorn Leelasantitham received the

B.Eng. degree in Electronics and

Telecommunications and the M.Eng.

degree in Electrical Engineering from

King Mongkut’s University of Technology

Thonburi (KMUTT), Thailand, in

1997 and 1999, respectively. He received

his Ph.D. degree in Electrical Engineering

from Sirindhorn International Institute

of Technology (SIIT), Thammasat

University, Thailand, in 2005. He is currently

the Assistant Professor in Department of Computer and

Multimedia Engineering, School of Engineering, UTCC, Thailand.

His research interests include analog circuits, image processing,

medical images, computer graphics, AI, neural networks,

embedded systems and robotics.

Supaporn Kiattisin received B.Eng.

in Computer Engineering from Chiangmai

University in 1995, M.Eng. in

Electrical Engineering and Ph.D. in

Electrical and Computer Engineering

from King Mongkuts University of Technology

Thonburi (KMUTT), Bangkok,

Thailand. She currently works at

Computer Engineering and Multimedia,

School of Engineering, University of the

Thai Chamber of Commerce (UTCC).

Her research interests include medical imaging, computer vision

and modeling. She is member of TESA, ThaiBME, IEICE

and IEEE .


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 43

Treatment Planning Technique Applying the

Combined CT Imaging and Computational

Fluid Dynamics (CFD) Analysis

K. Hemtiwakorn ∗ , N. Phoocharoen, Guest members,

V. Mahasittiwat, and M. Sangworasil, Members

ABSTRACT

Combined medical imaging with computational

fluid dynamics (CFD) analysis was used to demonstrate

and evaluate airflow in human nasal cavity.

The processes of this study are segmentation, meshing,

solving, and post-processing. Firstly, Mimic

10.01 software was used to segment the nasal cavity.

Secondly, Pro-STAR/amm software was employed to

generate trimmed mesh of 338,496 elements. Finally,

computational grid model was imported to STAR-CD

version 3.24 for numerical computation, and visualize

the solution by post-processing. The results show

the nasal airflow simulation in both normal and abnormal

human noses. It can be seen that for the

normal nose breathing, the velocity magnitude of airflow

was greatest in a nasal valve area which is the

narrowest area of the human nose. Also, the air flow

commonly passes through the main nasal passage and

middle meatus areas, and velocities in both sides are

not different. For the abnormal nose of nasal septum

deviation, the result showed that the velocity

magnitude inside the narrowed left side is very high

and greater than the right side about 50-90 percent.

Therefore, combined CT data and CFD analysis is

very useful for doctor to evaluate the physiology and

phenomena of airflow inside the patient’s nose. Rhinosurgion

could be employed those technique for the

preparing the rhinosurgical planning. Nevertheless,

the experimental validation is not reported in this paper

but will be published in the further publication.

In conclusion, this study is the important beginning

of an applied CFD analysis with medical imaging.

CT imaging combines with CFD analysis could be

useful for rhinologist as a nose function evaluation

technique. CFD based on CT imaging could be applied

as a tool for the treatment planning.

Keywords: CFD, Nasal airflow, Treatment planning

* Corresponding author.

Manuscript received on December 25, 2009.,

K. Hemtiwakorn is with the Biomedical Engineering, Faculty

of Engineering, King Mongkut’s Institute of Technology

Ladkrabang, Bangkok, Thailand.

E-mail addresses: hemtiwakorn@gmail.com (K. Hemtiwakorn)

1. INTRODUCTION

Nose is an upper part of the respiratory tract which

is the opening of a human airway. Three main functions

of nose [1] are filtering, smelling, and optimization

temperature and humidity to the ambient air

before going through the lung. Since, nasal cavity of

human is very narrow and complicate,visualization

of nasal cavity is very difficult. The major symptoms

such as difficult breathing, loss of smell, or sinus infection

can increase the resistance to flow [2], and also

may be occur from the obstruction or nasal septum

deviation. The methods of rhinomanometry, acoustic

rhinometry, or medical imaging, such as CT or

MRI, can help the doctor to diagnose the pathology

in human nose, and treatment planning. However,

previous methods can not demonstrate the function

of nose, such as airflow, in normal or abnormal case.

CFD based on CT imaging and simulation software

is the useful technique to evaluate the airflow direction,

velocity, and pressure inside the nasal cavity for

pre and post operations. The combination of medical

imaging technique, such as CT or MRI, and computational

fluid dynamics (CFD) analysis could be applied

for the diagnosis, treatment planning, and evaluate

the physiology of the nose after operation. Thus, this

study proposed the combined CT imaging and CFD

analysis as a tool help doctor for planning the nose

operation.

2. NUMERICAL MODELING BASED ON

CT IMAGING

2.1 GEOMETRIC MODEL CONSTRUC-

TION

The first process of this research is construction

the geometric 3-dimensional nose model by using the

computed tomographic (CT) data obtained from volunteer

and segmentation software. DICOM data of

CT images were imported into MIMICS 10.01 software

(Materialize, USA) for segmenting the nasal

airway excluded paranasal sinus. A threshold level

range of about -1024 to -188 was applied to the area

consisting of air. After automatic segmentation with

threshold level of brightness, the manual segmentation

should be performed not only in axial images but

also in coronal and saggital images as shown in Fig.1.


44 K. Hemtiwakorn et al: Treatment Planning Technique Applying the Combined CT Imaging ... (43-49)

Finally, 3-dimensional nose model was reconstructed

as demonstrated in Fig.1.

2.2 COMPUTATION MESH GENERATING

The mesh generating software called Pro-STAR/amm

software (CD-Adapco, UK) was used for this purpose.

Unstructured mesh was generated in this study.

Also, it is necessary to create at least two cell layers

immediately next to the inner boundary of the

3-dimension model to obtain a stable and convergent

solution. Therefore, two layers were located near to

the wall. Fig.2 shows the trimmed-cell mesh with two

layers cell structure near the model surface.

2.3 FLUID DYNAMICS CONDITION/ GOV-

ERNING EQUATION

Airflow inside a human nasal cavity is in a dynamic

state. It decelerates and accelerates from a resting

state during normal breathing. The air is considered

as incompressible and Newtonian with constant fluid

properties. A numerical solution of the mean flow requires

resolving the mass and momentum conservation

equations (the “Navier-Stokes equations”) solved

by STAR-CD are in Cartesian tensor notation [1] as

follows:


∂x j

(ρ · u j ) = 0 (1)


(ρ · u j · u i − τ ij ) = − ∂p (2)

∂x j ∂x j

where x i is the Cartesian coordinate (i = 1,2,3)

u i = absolute fluid velocity component in direction

x i ,

p = piezometric pressure = p s − ρ 0 g m x m where ps

is static pressure, ρ 0 is reference density, the g m are

gravitational acceleration components and the x m are

coordinates relative to a datum where ρ 0 is defined

ρ = density

τ ij = stress tensor components

Standard low Reynolds number two-equations k−ε

model is the turbulence model applied in this study.

2.4 BOUNDARY CONDITION AND NU-

MERICAL CONTROL

The computational grid nose model was imported

into the commercial CFD software package, STAR-

CD 3.24 of Adapco, UK, which solve the finite volume.

Discretization of the governing equations was

conducted using a Mono Tone Advection and Reconstruction

Scheme (MARS), which is a secondorder

accurate differencing scheme. Of all differencing

schemes available in STAR-CD, MARS was chosen

for its optimal sensitivity to the solution accuracy

and skewness of the mesh structure [2]. The

Semi-Implicit Method for Pressure-Linked Equations

(SIMPLE) algorithm [3] was used to manage the

pressure-velocity coupling. To stabilize the solution,

under-relaxation factors were used for all the basic

variables (0.5 for momentum and turbulent equations

and 0.1 for pressure). The sets of linearized and discretized

equations for all variables were solved using

the algebraic multigrid method for improving the calculation

performance of trimmed meshes [2]. We assumed

that the uniform velocity profile and the axial

component of velocity were perpendicular to the flow

inlet faces. The boundary conditions at the nostrils

and the nasopharynx were set to the static pressure

= 0 and outlet boundary face is fixed mass flow rate

(kg/s). Turbulence intensity was set to 10% and the

dissipation rate at the inlet boundaries was set with

a dissipation length scale of 1 cm. Convergence was

judged by monitoring the magnitude of the absolute

residual sources of mass and momentum, normalized

by the respective inlet fluxes. The iteration is continued

until all above residuals fall below 0.001

3. INSPIRED NASAL AIRFLOW SIMULA-

TION RESULTS

In this study, the normal breathing in human was

studied in the first investigation. Next, the abnormal

breathing during the pathology in the patient’s

nose was studied. Comparison between normal and

abnormal breathing are useful for doctor to evaluate

the patient status and treatment planning. The results

of the airflow simulation in both normal and

abnormal noses are demonstrated in the following.

3.1 NORMAL BREATHING IN HUMAN

NOSE

Computer modeling was constructed using CT

data obtained from a female patient. CT scanning

was performed on a General Electric LightSpeed VCT

(Wisconsin, USA) scanning station with the following

parameters: 120 kVp, 200.0 mA, rotation time of

0.9 s, thickness 1.25 mm. To construct an anatomical

3-dimensional model of nasal cavity, 48 axial sections

from a computed tomographic image of a single subject

were used for the numerical modeling. The computations

were performed using a personal computer

with an Intel Core 2 Quad 2.4 GHz CPU and 6 GB

of memory, which typically took 2 hours per run to

complete.

The velocity magnitude of airflow in right nasal

cavity is visualized in saggital view as shown in Fig 3.

From this figure, it can be seen that the greatest velocity

magnitude occurs in nasal valve area, because

it is the narrowest area of the nose. Also, high values

and variation of velocity magnitude appears in the

anterior part of nasal airway in front of a turbinate

region. The flow was slower when passed through

the main nasal passage and turbinate regions because

there were wider than the anterior part of the nasal


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 45

Fig.1: CT images in 3 planes of coronal, axial, and saggital were used for segmentation. Magenta color

areas demonstrate a nasal airway (exclude paranasal sinus) after automatic and manual segmentations. 3D

reconstructed nose model is shown in this figure.

Fig.2: Trimmed cell mesh model was performed after constructing the 3D model obtained from CT images.

Cross-sectional grid models in coronal and saggital planes are shown in this figure.

cavity. For the airflow in turbinate meatus region,

almost flow pass through the middle meatus which is

wider than another meatus. Thus, slow flow passes

through the inferior and superior meatus.


46 K. Hemtiwakorn et al: Treatment Planning Technique Applying the Combined CT Imaging ... (43-49)

Fig.3: Velocity magnitude of the right nasal cavity is shown in a saggital plane. Landmark line of cutting

plane location is demonstrated.

Fig.4:

Coronal cutting planes of nasal model.

Moreover, cutting model in coronal plane to visualized

velocity magnitude in specific regions was

presented. The velocity magnitudes of airflow were

demonstrated in six planes as shown in Fig.4. Plane

A is a nostril. Plane B represents the nasal valve

area. Plane C is the head of inferior turbinate region.

Plane D is the beginning of middle turbinate region.

Plane E represents the middle part of nasal cavity,


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 47

Fig.5: The velocity magnitude (m/s) of nasal airflow in each coronal plane (A-F) obtained from normal

nose model.

and plane F is the beginning of superior turbinate

region.

The velocity magnitudes in each plane are presented

in Fig. 5. The results show that velocities increase

immediately in the nasal valve area. The highest

velocity magnitude of 1.88 m/s occurs in plane B

which is the nasal valve area. Moreover, it can be

seen that airflow commonly passes through the main

nasal passage and the middle meatus. However, slow

flow of 0.13-0.26 m/s appears in the inferior and superior

meatus. From the Fig.5, it can be seen that

flow commonly passed near the nasal septum more

than diffused to the nasal wall.

3.2 ABNORMAL BREATHING IN DEVI-

ATED NASAL SEPTUM

Computer modeling was constructed using CT

data obtained from a male patient with the nose obstruction

due to the nasal septum deviation. To construct

an anatomical 3-dimensional model of nasal

cavity, 64 axial sections from CT images of a single

subject were used for the numerical modeling.

From the CT data, the images show that nasal septum

is deviated to the narrowed left side of patient’s

nose as shown in Fig 6. To confirm the diagnosis

results of nasal septum deviation and narrowed left

side, rhinoscan was performed in the same patient.

The rhinoscan result shows that the left side is very

narrow as compare with the right side as shown in

Fig 7. Also, the inspired volume flow rate in a left

side is lesser than a right side as 34.6 cm2/s and 219.3

cm2/s for left and right sides, respectively.

Fig.6: CT image obtained from a male patient of

nasal septum deviation.

The first airflow simulation result in abnormal nose

model is demonstrated in Fig 8. The figure shows the

velocity magnitudes of airflow inside a nasal cavity

with a deviated nasal septum and very narrow in the

left side of nasal cavity. It can be seen that the velocity

magnitudes of airflow inside the left nasal cavity

are greater than the right side, particularly for the

obstructive area approximately 1.5-2.0 m/s for a left

side and 1.2 m/s for a right side, respectively. As

comparing with the normal nose model shown in Fig.

5, it can be seen that the velocity magnitude of airflow


48 K. Hemtiwakorn et al: Treatment Planning Technique Applying the Combined CT Imaging ... (43-49)

inside the both nasal cavities is not much difference.

This study presents nasal airflow simulation in

normal and abnormal adult noses by combining CT

imaging with computational fluid dynamics (CFD)

analysis. It enables us to study the physiology of

nasal breathing. The advantage of this method is the

airflow simulation on a realistic model derived from

CT imaging. Moreover, combined CT data and CFD

analysis is useful for doctor to diagnosis, evaluate the

phenomena of airflow inside the patient nose, treatment

planning, and pre-post operative airflow simulation.

6. ACKNOWLEDGMENT

The authors wish to acknowledge the facilities of

computer and software provided by the Computer

Service Center of King Mongkut’s Institute of Technology

Ladkrabang (KMITL), Bangkok, Thailand.

Moreover, authors would like to thank Mahidol University

and SWU, Bangkok, Thailand, for their supporting

the CT images. We would like to thank Assoc.

Prof. Manas Sangworasil for his kindly suggestion,

support, and encouragement throughout this research.

Finally, the author wish to thank the Office

of the Higher Education Commission, Bangkok, Thailand

for the financial support during this research.

Fig.7: Rhinoscan result showed that a left side of

nose is narrow.

4. APPLICATION OF CFD FOR A TREAT-

MENT PLANNING

To correct the abnormal nasal cavity of the patient,

treatment technique of surgery is an invasive

technique but necessary particularly for the case used

in this research. Therefore, preparation the surgical

planning is very important. Combined CT data and

CFD analysis is very useful for doctor to evaluate the

physiology and phenomena of airflow inside the patient’s

nose. Doctor can understand the affect of abnormal

nose geometry to the direction, velocity, and

pressure of airflow inside the nose. Moreover, rhinosurgion

could be applied those technique for the

rhinosurgical planning, and also visualize the nasal

airflow obtained from the process of pre-operation

preparation. Nevertheless, nasal airflow obtained

from pre-post operation can be calculated by a CFD

technique but does not show in the present. Also,

the experimental validation of nasal airflow simulation

obtained from this study will be reported in the

future publication.

5. CONCLUSION

References

[1] Z.V.A. Warsi, Conservation form of the Navier-

Stokes Equations in General Nonsteady Coordinates,

AIAA Journal, 19, pp.240-242, 1981.

[2] STAR-CD 3.2 Users’ Guide. Computational Fluid

Dynamics Ltd., 2004.

[3] S.V. Patankar, D.B. Spalding. A Calculation Procedure

for Heat, Mass and Momentum Transfer in

Three-Dimensional Parabolic Flows. Int. J. Heat

Mass Tranfer, 15, pp. 1778-1806, 1972.

K. Hemtiwakorn obtained her B.Sc

and M.Sc in radiological technology in

2005 and 2008, respectively from the

Mahidol University, Bangkok, Thailand.

She is currently pursuing her Ph.D in

biomedical engineering, Faculty of Engineering,

Bangkok,Thailand. She also

the research assistant in Ramathipbodi

Hospital in several research projects

about radiological technology and applied

biomedical engineering. Her research

interests include computational fluid dynamics (CFD)

in human body, biomechanics, and apply the CFD for evaluate

and treatment planning in the ENT patient.

N. Phoocharoen obtained his B.Eng

in Mechanical Engineering from King

Mongkut’s Institute of Technology Ladkrabang

(KMITL), Bangkok, Thailand.

He is currently pursuing his M.Eng

in Mechanical Engineering, KMITL,

Bangkok, Thailand. He has more than

10 years experience in CFD research in

both mechanical and biomedical engineering.

He is now the research assistant

in the Computer Service Center,

KMITL, Bangkok, Thailand.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 49

Fig.8: The velocity magnitude (m/s) of airflow inside the abnormal nose model of deviated nasal septum in

each coronal plane (A-H).

V. Mahasithiwat obtained his MD

from Prince of Songkla University

(PSU). He is currently the ENT expert

in Faculty of Medicine, Srinakharinwirot

University. His research interests

include ENT technology and development

of earring aids, nose operation

technique, and eye tracking.

M. Sangworasil was born in Bangkok,

Thailand in 1951. He received the Bachelor

of engineering and Master o Engineering

from King Mongkut’s Institute

of Technology at Ladkrabang, Bangkok,

Thailand in 1973 and 1977 respectively,

and the D. Eng (Electronics) from Tokai

University, Japan, in 1990. Following

his graduate studies, he worked almost

28 years at Electronic De- partment,

Faculty of Engineering, King Mongkut’s

Institute of Technology at Ladkrabang, Bangkok where he is

currently an associate professor. His research interest are in the

area of image process with emphasis on Imag Reconstruction,

3D modeling, Image Classication and Image Filtering.


50 K. Jaruwongrungsee et al: Analysis of Quartz Crystal Microbalance Sensor Array with ... (50-54)

Analysis of Quartz Crystal Microbalance

Sensor Array with Circular Flow Chamber

K. Jaruwongrungsee 1,2,∗ , T. Maturos 1 , P. Sritongkum 1 ,

A. Wisitsora-at 1 , Guest members, M. Sangworasil 2 , and A. Tuantranont 1 , Members

ABSTRACT

In this paper, quartz crystal microbalance

(QCM) sensor array integrated with circular

poly(dimethylsiloxane) (PDMS) chambers is developed

for flow injection based bio-sensing. An

array of QCM sensors was fabricated on a single

quartz substrate by Cr/Au sputtering through

shadow masks and integrated with PDMS chambers,

made by PDMS based microfluidic fabrication technology.

Gold electrodes of QCM sensors were functionalized

with carboxylic group and flow injection

analysis was conducted for protein G binding. The

resonance frequencies for four sensors were continuously

monitored during protein G injection in a constant

flow of PBS buffer solution. It was found that

there was a significant variation in resonance frequency

shift responses of identical QCM sensors in

the circular QCM chamber. The cause of such undesired

variation was then analyzed by fluid dynamic

simulation. The simulation results reveal that the

flow in a circular-shaped QCM chamber is primarily

turbulent. In addition, the degree of turbulence is

increased with flow rate. Thus, sensors at various locations

see different sample dispersions causing their

sensing behaviors to be significantly different.

Keywords: Quartz crystal microbalance; QCM

1. INTRODUCTION

Quartz crystal microbalance (QCM), an ultrasensitive

mass sensor based on piezoelectric effect, is

one of the most powerful methods for chemical and

biological sensing. QCM has a wide range of applications

in many fields such as thin-film measurement,

chemical analysis [1], gas sensor [2], humidity sensor

[3-4], and biosensor [5]. When the electric power

is applied to a pair of electrodes, sandwiched between

quartz crystal, mechanical force is generated

* Corresponding author.

Manuscript received on January 29, 2009.,

1 1Nanoelectronics and MEMS Laboratory, National Electronics

and Computer Technology Center, Thailand

telephone: +662-564-6900 ext.2108, Fax: +662-564-6756

2 Department of Electronics, Faculty of Engineering, King

Mongkut’s Institute of Technology Ladkrabang, Thailand

E-mail addresses: kata.jaruwongrungsee@nectec.or.th

(K.Jaruwongrungsee) adisorn.tuantranont@nectec.or.th

(A. Tuantranont)

via piezoelectric effect. With this effect, quartz crystal

will resonate at its natural frequency by positive

feedback through oscillator circuit. The oscillation

frequency depends on many factors including quartz

thickness, quartz density, type of cut, ambient conditions

(temperature, pressure, humidity, etc) and most

importantly deposited mass.

For the reason that oscillated frequency can be

changed as a function of mass, quartz crystal has

been used as a kind of mass sensor. AT-cut, the most

popular cut of quartz crystal, was chosen on account

of its frequency stability around room temperature.

In 1959, Sauerbrey derived the equation of frequency

shift of the quartz resonator in gas phase [6]:

∆f = −

2f o

2 ∆M


ρq µ Q A

(1)

where: ∆f is the frequency shift of the resonator,

f0 is the fundamental frequency, ρ q is the density

of quartz (2.648 g/cm 3 ), µ q is the shear modulus of

quartz (2.947×1011 g/cm×s 2 ), ∆M is the mass deposited

on the surface of electrode and A is piezoelectrically

active area (2/(ρ q µ q ) 1/2 can be expressed

as a constant, k, which is equal to 2.26×10 −7 ). This

equation can describe only the added mass rigidly

deposited on the electrode surface. For the attached

liquid molecules, the frequency shift can be described

by equation of Kanazawa and Gorgon [7], which use

to find the frequency shift based on the liquid properties

of the liquid on electrode surface:

∆f = −f 3 2 o


ρL η L

πρ q µ q

(2)

where: ρ L and η L are the density and absolute viscosity

of the liquid, respectively.

In the last few years, many researchers interested

in QCM based sensor-array for advanced sensing

applications, including electronic nose, electronic

tongue and biosensor array. However, majority of

reported QCM sensor arrays are still based on simple

combination of single QCM sensors from different

substrates [8-10]. The main problem of

this scheme is the mismatch characteristics among

QCM devices due to different piezoelectric properties,

quartz-thickness, temperature, pressure, mass

and material properties of electrode layers. To min-


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 51

(a)

(b)

Fig.1:

Schematic of QCM sensor array (a) electrode layout and (b) electrodes superimposed by PDMS

imize the matching problem, QCM sensors in an array

should be fabricated on the same quartz substrate

under the same process parameters and conditions

[11-12]. In addition, the effect of environmental

conditions including temperature and humidity

can be greatly reduced by employing one

QCM in the matched array as the reference sensor

so that the change in QCM characteristics due

to ambient parameter can be effectively canceled.

In this work, a QCM based biosensor array is integrated

with poly(dimethylsiloxane) (PDMS) microfluidic

platform for flow-injection based detection.

2. SENSOR FABRICATION

2.1 QCM Sensor Array Fabrication

An array of QCM sensors was fabricated on a single

disc of 1-inch 5 MHz AT-cut quartz crystal substrate.

The number of QCM sensors on a quartz substrate

was designed as 4 and the patterns of electrode

pair were laid out symmetrically within 1-inch

circle as illustrated in Figure 1 (a). Since QCM

electrodes were located on both sides of quartz disc,

it is difficult to make electrical connection for testing.

To circumvent this problem, the electrode layouts

for both sides were made different. The back

side patterns contain contact pads for both electrodes

while the front side patterns have no contact

pad but have connection line that run to the pad

onto the other side of quartz disc through the circumference.

The Cr/Au electrode was deposited on

quartz substrate by sputtering through electroplated

microshadow-mask. Microshadow-mask was fabricated

by Ni electroplating on photoresist-patterned

stainless-steel plate. The standard dry-film photolithography

process was used to produce photoresist

pattern of electrode designs on stainless steel

plate. Uncovered area on stainless-steel plate was

then coated by 40 m thick Ni film by electroplating

process. Ni electroplating was conducted in Nickel

sulphate plating solution for 4 hours. Nickel shadow

masks were obtained by removing the photoresist by

sodium hydroxide solution and detached the nickel

film from stainless steel plate.

Before QCM electrode deposition, blank quartz

disc substrates were cleaned in piranha solution (1:4

mixture of 50% H2O2 and 97% H2SO4) at 120 oC

for 10 min. The shadow mask was then aligned and

attached on a blank quartz disc by a permanent magnet.

The chromium (Cr)/ gold (Au) layers were deposited

on one side of quartz substrates by sputtering

through the first set of electroplated microshadowmasks.

The Cr and Au layers were successively sputtering

under argon dc plasma. Before each sputtering

run, the substrate was cleaned by 75W RF plasma for

5 min to improve adhesion to underlying material.

The sputtering pressure, sputtering current and time

for chromium were 3x10-3 mbar, 0.2 A and 2 min, respectively.

Next, gold was sputtered under the same

current and pressure for 10 min. The 50 nm-thick Cr

and 300 nm-thick Au layers were obtained. The other

Cr/Au layers were then sputtered on the other side of

quartz disc through the second set of microshadowmasks.

The second shadow masks were aligned to the

first pattern via a cross marker at the center of the

disc.

2.2 PDMS Micro Chamber Fabrication

Silicon wafer were cleaned in piranha solution at

120 oc for 10 min, carefully rinsed several time in

deionized water and dried with gentle stream of air.

After that silicon wafer were dehydrated at 150-200

oC for 10 min. SU-8 photoresist was spin-coated on

silicon wafer using a spin coater (Laurell technologies

corp. model WS-400A-6NPP), then soft baked

to remove all the solvent in the layer. The photoresist

coated wafers were exposed using MJB4 mask

aligner (SUSS microtec) then post-baked in order to

selectively cross-link the exposed portions of the film.

The sample was left in the desiccators to cool down

slowly at room temperature for more than 13 hours.


52 K. Jaruwongrungsee et al: Analysis of Quartz Crystal Microbalance Sensor Array with ... (50-54)

Finally sample was developed, cleaned with deionized

water and isopropyl alcohol and gently dried with air.

Spin speed, exposure time, baked time and developing

time were optimized to achieve a smooth surface

on mold. The SU-8 mold thickness was investigated

by interferrometer (Polytec MSA400), the measured

result show that the mold thickness is 214.9 µm.

Sylgard 184 Silicone Elastomer kit (Dow Corning),

consisting of PDMS was prepared by mixing the precursors

sylgard with a curing agent at a ratio of 10:1

by volume. The prepolymer mixture was degassed at

20-50 mTorr in ambient temperature desiccator with

a mechanical vacuum pump for 10 min to remove any

air bubbles in the mixture. PDMS mixtures were

gradually poured onto the SU8 master mold to the

height over the depth of designed chamber. Next,

PDMS slab was cured at 80 oc for 3 hour. Finally, it

was peeled-off from the mold. Figure 2 shows the photograph

of fabricated QCM sensor array with PDMS

chamber.

with circular design. The fluid dynamic simulation

was carried out by imposing a constant sample flow

rate condition throughout the chamber.

Table 1: Measured frequency shift from the experiment

Sensor Number Measured Frequency Shift (Hz)

1 52

2 44

3 163

4 73

Fig.3:

Meshed circular design of sensor chamber

Fig.2:

QCM sensor array with PDMS chamber

3. EXPERIMENTAL RESULTS AND DIS-

CUSSIONS

The sensor array was prepared for protein G binding.

The surface of gold electrode was functionalized

with carboxylated polyvinyl chloride (PVC-COOH)

by self-assembly monolayer (SAM) technique. Flow

injection measurement was conducted for protein G

binding. The resonance frequencies for four sensors

were continuously monitored during protein G injection

in a constant flow of PBS buffer solution. It was

found that there was a significant variation in resonance

frequency shift responses of identical QCM

sensors, as shown in table 1. In order to analyze this

problem, computational fluid dynamics (CFD) simulation

is used to design and simulate sample transport

in flow injection QCM sensing system.

The 3D-simulation of fluid flow through QCM sensors

was performed by ANSYS program. Figure 3 illustrates

the finite element model of QCM chamber

Figure 4 shows sample dispersion trajectories at

various flow rates including 50 µl/min and 100

µl/min. Simulation results show that the flow in

this circular-shaped QCM chamber design is primarily

turbulent. In addition, the degree of turbulence

is increased with flow rate. The sample dispersion

especially on both far sides of chamber where sensor

electrodes are located is nonlinear. The sensors

at various locations see different sample dispersions

causing their sensing behaviors to be significantly different.

Thus, the simulation results can explain the

observed signal variation among four QCM sensors

in the circular PDMS chamber. In order to solve this

problem, QCM chamber will be redesigned with different

geometries and the results will be presented

elsewhere.

4. CONCLUSIONS

In conclusion, quartz crystal microbalance (QCM)

sensor array integrated with poly(dimethylsiloxane)

(PDMS) chambers has been designed and fabricated

for flow injection based bio-sensing. It was found

that there was a significant variation in resonance

frequency shift responses of identical QCM sensors

in the circular QCM chamber. The cause of such


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 53

(a)

(b)

Fig.4: The geometries of turbulent flow effect inside

circular chamber in 3D simulations and velocity variation

over the sensor surface at different flow rate (a)

50 µl/min, (b) 100 µl/min

undesired variation was then analyzed by fluid dynamic

simulation. Fluid dynamic simulation of QCM

chamber shows that the sample flow in circular QCM

chamber is turbulent. The results can explain the

observed significant variation in sensing responses of

identical QCM sensors. In the future, QCM chamber

will be redesigned with different geometries to

obtain laminar flow through all sensor electrodes so

that every sensor experiences the same sample dispersion

under a constant flow condition.

5. ACKNOWLEDGMENT

This project is funded by NECTEC’s Sensor Platform

Technology under QCM sensor for food safety

project. The authors would like to acknowledge with

gratitude to Dr. Chamras Promptmas at Mahidol

University for information supporting. Moreover, we

would like to thanks Asia Institute of Technology

(AIT) for the use of simulation software and hardware.

References

[1] J. Rabe, S. Buttgenbach, J. Schroder, and P.

Hauptmann, “Monolithic miniaturized quartz

microbalance array and its application to chemical

sensor systems for liquids,” IEEE Sensors

Journal, Vol. 3, pp. 361 - 368, August 2003.

[2] Masashi Kikuchi, Katsuya Omori, and Seimei

Shiratori, “Quartz Crystal Microbalance (QCM)

Sensor for Ammonia Gas using Clay/ Polyelectrolyte

Layer-by-Layer Self-Assembly Film,”

IEEE Sensors Proceedings 2004, vol. 2, pp. 718-

721, October 2004.

[3] K. Jaruwongrungsee, A. Wisitsoraat, T. Lomas

and A. Tuantranont, “Humidity sensor utilizing

multiwalled carbon nanotubes coated quartz

crystal microbalance,” The 2nd IEEE Nanoelectronics

Conference, pp. 961-964, March 2008.

[4] H. Ito, S. Kakuma, R. Ohba, and K. Noda,

“Development of a humidity sensor using quartz

crystal microbalance,” SICE Annual Conference

2003, pp. 1175-1178, August 2003.

[5] Robert B. Towery, Newton C. Fawcett, and Jeffrey

A. Evans, “Determination of chloroplast

DNA in a cultured soybean line using a QCM

biosensor,” IEEE Sensors Journal, Vol. 4, pp.

489 - 493, August 2004.

[6] G. Sauerbrey, “Use of vibrating quartz for thin

weighing and microweighing,” Z. Phys., vol.155,

p.260, 1959.

[7] K.K. Kanazawa and J.G. Gordon, “The oscillation

frequency of a quartz resonator in contact

with a liquid,” Anal. Chim. Acta., vol. 175, p.99,

1985.

[8] A.Yuwono, and P. Schulze Lammers, “Performance

Test of a Sensor Array - Based Odor

Detection Instrument,” Agricultural Engineering

International: the CIGR Journal of Scientific

Research and Development, May, 2004.

[9] Pengchao Si, John Mortensen, Alexei Komolov,

Jens Denborg, and Preben Juul Moller, “Polymer

coated quartz crystal microbalance sensors

for detection of volatile organic compounds in

gas mixtures,” Analytica Chimica Acta, Vol. 597,

pp. 223-230, 2007.

[10] I.A. Koshets, Z.I. Kazantseva, and Yu.M.

Shirshov, “Polymer films as sensitive coatings

for quartz crystal microbalance sensors array,”

Semiconductor Physics, Quantum Electronics &

Optoelectronics, Vol. 6, pp. 505-507, 2003.


54 K. Jaruwongrungsee et al: Analysis of Quartz Crystal Microbalance Sensor Array with ... (50-54)

[11] A. Palaniappan, Xiaodi Su, Francis and E.H.

Tay, “Quartz sensor array using mesoporous silica

hybrids for gas sensing applications,” Proc.

2005 IEEE Sensors, pp. 1312-1315, 2005.

[12] K. Jaruwongrungsee, T. Maturos, A. Wisitsoraat,

C. Karuwan, T. Lomas, A. Tuantranont,

“Fabrication and Characterization of QCM Gas

Sensor Array on Single Quartz Disc,” The 4th

Asia Pacific Conference on Transducers and Micro/Nano

Technologies (APCOT’08 ), pp. 168,

June 2008.

Kata Jaruwongrungsee received the

B.Eng. and M.Eng. degree in electronics

engineering from King Mongkut’s

Institute of Technology Ladkrabang

(KMITL), Thailand, in 2003 and 2005

respectively. Since 2006, he has

been with the Nanoelectronics and

MEMS Laboratory, National Electronics

and Computer Technology Center

(NECTEC), Thailand, as an assistant

researcher. Presently, he is also doing

his Ph.D. on electronics engineering at KMITL. His research

is mainly focused on QCM based gas sensors and biosensors.

Anurat Wisitsora-at received his

Ph.D., M.S. degrees from Vanderbilt

University, TN, U.S.A., and B. Eng degree

in electrical engineering from Chulalongkorn

University, Bangkok, Thailand

in 2002, 1997, and 1993, respectively.

His research interests include microelectronic

fabrication, semiconductor

devices, electronic and optical thin film

coating, sensors, and micro electromechanical

systems (MEMS).

Manas Sangworasil was born in

Bangkok, Thailand in 1951. He received

the bachelor of engineering and Master

o Engineering from King Mongkut’s

Institute of Technology at Ladkrabang,

Bangkok, Thailand in 1973 and 1977 respectively,

and the D. Eng (Electronics)

from Tokai University, Japan, in

1990. Following his graduate studies, he

worked almost 28 years at Electronic Department,

Faculty of Engineering, King

Mongkut’s Institute of Technology at Ladkrabang, Bangkok

where he is currently an associate professor. His research interest

are in the area of image process with emphasis on Image

Reconstruction, 3D modeling, Image Classification and Image

Filtering.

Thitima Maturos received the B.S.

degree in physics from Thammasat University,

Pathumthani, Thailand in 2002

and the M.S. degree in Physics from

Mahidol University, Bangkok, Thailand

in 2006. Since 2006 she has been

with Nanoelectronics and MEMS Laboratory,

National Electronics and Computer

Technology Center (NECTEC),

Thailand, where she is now assistant researcher.

Her research interests include

microfabrication, cell manipulation and Lab-on-a-chip.

Pornpimol Sritongkum received her

Ph.D. in electroanalytical chemistry

from Cranfield University (United Kingdom)

in 2002. Her current research

is focused on the surface modification

of QCM- and electrochemical biosensors

and on the development of development

of novel diagnostic devices.

Adisorn Tuantranont received the

B.S. degree in electrical engineering

from King Mongkut’s Institute of Technology

Ladkrabang, Thailand, in 1995,

and the M.S. and Ph.D. degrees in

electrical engineering (laser and optics)

from the University of Colorado at

Boulder in 2001. Since 2001, he has

been the director of the Nanoelectronics

and MEMS Laboratory, National Electronic

and Computer Technology Center

(NECTEC), Pathumthani, Thailand. He also serves on

the founding committee of the National Nanotechnology Center

(NANOTEC), the first nanotechnology center in Thailand,

under the Ministry of Sciences and Technology. His research

interests are in the area of microelectro-mechanical systems

(MEMS), optical communication, laser physics, microfabrication,

electro-optics, optoelectronics packaging, nanoelectronics,

and lab-on-a-chip technology. He has authored more than 60

international papers and journals and holds five patents. Dr.

Tuantranont received the Young Technologist Award in 2004

from the Foundation for the Promotion of Science and Technology

under the patronage of H. M. the King.


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 55

Muscular-Contraction Classification :

Comparison Study Between Independent

Component Analysis and Artificial Neural

Network

D. Sueaseenak 1∗ , T. Chanwimalueang 2 ,

W. Iampa 1 , Guest members, and M. Sangworasil 1 , Member

ABSTRACT

We developed a multi-channel electromyogram acquisition

system using PSOC microcontroller to acquire

multi-channel EMG signals. An array of 4 x 4

surface electrodes was used to record the EMG signal.

The obtained signals were classified by a backpropagation-type

artificial neural network. B-spline

interpolation technique has been utilized to map the

EMG signal on the muscle surface. The topological

mapping of the EMG is then analyzed to classify

the pattern of muscle contraction using independent

component analysis. The proposed system was successfully

demonstrated to record EMG data and its

surface mapping. The comparison study of muscular

contraction classification using independent component

analysis and artificial neural network demonstrates

shows that performance of ANN classification

is as comparable as that of the ICA. The computational

time of ANN is also less than that of the ICA.

Keywords: Electromyography, Principal Component

Analysis, Independent Component Analysis, Artificial

Neural Network

1. INTRODUCTION

Electromyography (EMG) is the study of muscle

electrical signals. EMG is sometimes referred to as

myoelectric activity. Many muscular abnormalities

such as muscular dystrophy, inflammation of muscle,

peripheral nerve damages could results in an

abnormal electromyogram [1-6]. Figure 1 shows a

schematic representation of muscle units and its components.

EMG can be recorded by two types of electrodes;

invasive electrode the so-called wire or needle

electrodes and non-invasive electrode the so-called

* Corresponding author.

Manuscript received on December 25, 2009.,

1 D. Sueaseenak, W. Iampa and M. Sangworasil, Department

of Electronics, Faculty of Engineering, King Mongkut’s

Institute of Technology Ladkrabang, Thailand

2 T. Chanwimalueang, Biomedical Medical Engineering programme,

Faculty of Engineering, Srinakharinwirot University,

Thailand

E-mail addresses: emg7849@gmail.com (D. Sueaseenak)

Fig.1: Muscle unit [7]

surface electrode. Wire or needle electrodes records

individual muscle fiber action potentials which is an

ideal choice to evaluate the muscle activity [10]. However,

fine wire intramuscular electrodes require a needle

for insertion into the muscle and may cause a significant

pain. The choice of surface electrode is then

preferable. However, when EMG is acquired from

surface electrodes mounted directly on the skin, the

signal is a composite of all the muscle fiber action potentials

occurring in the muscles underlying the skin.

Estimating this force in general is a hard problem

due to difficulties in activating a single muscle in isolation,

isolating the signal generated by a muscle from

that of its neighbors, and other associated problems

[8-9]. The clinical application of EMG can be classified

into two mains categories. (i) Standard EMG [9]

is recorded from discrete sites on a muscle and thus

provides only a limited picture of the actual muscular

electrical activity in the vicinity of the recording electrode.

(ii) Array EMG recorded by an array electrode

which facilitates the clinical interpretation of electrical

activities through the mapping of these signals on

the muscle surface [10]. In this paper, a PSOC-based

multi-channel surface electrode array data acquisition

system is developed to acquire EMG data. The

EMG signals are then mapped using B-spline interpolation

technique. The EMG topological Mapping

is then used for classification of muscular contraction.

There exist a number of 2D-pattern classifications


56 D. Sueaseenak et al: Muscular-Contraction Classification : Comparison Study ... (55-62)

Fig.2:

Muscular contraction classification system

[10-13]. In this research, we compared the EMGcontraction

classification technique between independent

component analysis (ICA) and artifial neural

network (ANN) [15]. The results show that the ANN

classification yields as good performance as the ICA

in the faster computational time.

The paper is organized as follows: Section 2 is devoted

to the designed concept of multi-channel electromyogram

system. Section 3 briefly reviews fourierbased

features, Section 4 describes feature extraction

and topological mapping process. Section 5 briefly

introduces Independent Component Analysis. Section

6 briefly introduces Artificial Neural Network.

The experiment and results are shown in Section 7.

Discussion and Conclusion are provided in Section 8.

Fig.3:

system

Multi-channel electromyogram acquisition

2. DESIGN AND CONSTRUCTION OF

MULTI-CHANNEL EMG

EMG measurement is accomplished by the instrument

called electromyograph. The system, in general,

consists of instrumentation amplifier, notch filter, offset

adjustment, isolator, main amplification, and the

CRT display. The instrument amplifier is a front-end,

high CMRR differential amplifier which functions to

pick-up a low amplitude signal submersed in the highfrequency

noise. The notch filter gets rid of the 50Hz

noise while keeping the EMG signal intact. The offset

adjustment maintains the baseline level especially

during the subject’s motion. The function of isolator

is to separate the front-end section from the rear-end

section to protect the possible electrical shock to the

patient. The main amplification conditions the EMG

prior to be display with CRT. The complexity of the

electronic circuit becomes realized with the necessity

to monitor the multi-channel of EMG. Such complicate

designs, however, are made possible by the creation

of entirely reconfiguration and programmable

components the so-called Programmable System on

Chip Microcontroller (PSOCMicrocontroller).

The designed EMG system is capable of monitoring

16 channels of EMG simultaneously. Each channel

consists of 2 main parts; (i) EMG signal process-

Fig.4:

Raw EMG signal on laptop computer

ing unit and PSOC microcontroller. Figure 2 shows

the muscular contraction classification system Figure

3 shows Multi-channel electromyogram acquisition

system.

The EMG signal processing units consists of 3 subunits

(i) Instrumentation Amplifier. This subunit uses

the INA 2128 BUR-BROWN Integrated Circuit. The

IC can achieve a CMRR up to 120 dB and gain up to

1000.

(ii) Noise filter. The function of the filter is to


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 57

get rid of the 10-20 Hz noise which is classified as a 4. EMG FEATURE EXTRACTION AND

motion artifact.

MAPPING

(iii) Amplifier and Offset Adjustment. The objective

of this sub-unit is to Amplifier EMG signal and

maintains the appropriate offset voltage prior to interface

with the PSOC.

The PSOC microcontroller consists of 4 subunits

(i) PGA (Programmable Gain Amplification) This

subunit acts as the buffer and the main amplification

of EMG.

(ii) Low pass filter. The function of the filter is

to remove of the high frequency noise. The cut-off

frequency is at 500 Hz.

Fig.6: Feature extraction and mapping

(iii) DELTA-SIGMA. This subunit functions as a

8-bit analog to digital converter.

Figure 6 shows the feature extraction and mapping

process. Each of the 16 EMG channels will be

(iv) UART. This subunit functions to perform RS-

232 interfacing unit with PC

converted to frequency domain by taking the fourier

transform. The energy content of the EMG signal

3. CLASSICAL FOURIER-BASED FEATURESis then evaluated by computing area under the magnitude

squared of the fourier transform. The energy

Fourier transform is a useful technique that represents

a stationary signal in terms of a function of content on the 4x4 grid corresponding to the 4x4 electrode

shown in figure 7 is used for artificial neural

frequency by determining the frequency component.

The equation is represented as:

network classification. The 4x4 grid data was interpolated

to derive the 49x49 topological maps which

N−1


are later applied to ICA for muscular contraction classification.

Figure 8 shows the topographical mapping

X (f) = x (n) e (−j2πfn/N) (1)

of various muscular contractions.

n=0

Where x(n) is a sequence of EMG input and X (f)

is complex vector provided by the sinusoidal coefficients.

The classical Fourier transform was applied

to the EMG signal. Then, the power spectral density

of the energy at various frequencies was computed

from square magnitude of the Fourier transform as

represented in Equation 2. Figure 5 shows the energy

content of EMG signal in form of the power spectrum.

P S (f) = |X (f)| 2 (2)

Fig.7:

16 Channel electrode placements

Fig.5:

EMG Power spectrum

5. FROM PRINCIPAL COMPONENT ANAL-

YSIS TO INDEPENDENT COMPONENT

ANALYSIS

Principal Component Analysis (PCA) is a statistical

technique which used to describe a large dimensional

space with a relative small set of vectors. It

is a popular technique for finding patterns in data of

high dimension, and is used commonly in both face

recognition and image compression. [13] Application

of PCA to face recognition is known as Eigen face.

The Eigen face technique is a powerful yet simple solution

to the face recognition dilemma. It uses much

more information by classifying faces based on general

facial patterns. Here we focus on the application

of PCA for muscular-contraction classification

The procedure for using PCA is divided into 2

steps. (i) Training step and (ii) Classification step.


58 D. Sueaseenak et al: Muscular-Contraction Classification : Comparison Study ... (55-62)

C = 1 M ΦΦT (5)

(iv) Compute the Eigenvalue and Eigenvector of C

represented as

Cν i = µ i ν i (6)

where µ i is the corresponding Eigenvalue of Eigen

vector ν i

(v) Project each training set on the Eigenspace

using the operation

Ω = V · Φ (7)

Where V is the Eigen matrix where each row is

the Eigenvector ν i can be written as

Ω = [ω 1 , ω 2 , . . . , ω M ] (8)

Where ω i is the coefficient of the training map i th

The Classification step is as follow:

Project vector form of the tested topological mapping

matrix T p to the Eigenspace using equation (5)

to derive ω s as

ω s = V · [T n − ψ]

The tested topological mapping matrix is classified

to class k which minimized

ε 2 k = ‖ω s − ω k ‖ 2 (9)

with 1 ≤ k ≤ M

The goal of independent component analysis (ICA)

is to minimize the statistical dependence between the

basic vectors. Mathematically, we can write

W X = U (10)

Fig.8:

Topological mapping

The Training step is as follow:

(i) Convert each cropped topological mapping matrix

into a vector T i of length N (N= map width*map

height). For the M data set, we let the training set

represented by, {T 1 , T 2 , T 3 , ..., T M } where M is the

vector of N 2

(ii) Compute the mean vector Ψ and the set of

deviation from the mean vector Φ = [Φ i , Φ 2 , ...Φ M ]

which is defined as

Ψ = 1 M

M∑

T i (3)

i=1

Φ i = T i − Ψ (4)

(iii) Compute the covariance matrix C which is

defined as

ICA searches for a linear transformation W that

minimizes the statistical dependence between each

row of U. There exists a number of iterative algorithm

to solve for W [16,17]. Most of them are optimized

for the dependence criteria including Kurtosis, Negentropy,

etc.[18]. In this paper, we applied the well

known ICA algorithm the so-called InfoMax purposed

by Bell and Sejnowski [19]. The idea of InfoMax has

been applied to Eigenvector of PCA by Barlett et. al.

[20] by minimize the statistical dependence between

each row of U in

W V = U (11)

where V as an Eigen Basis matrix where each row

is the Eigen vector ν i defined in (4). The new basis

W −1 U is then used in place of V. The Projection

of each training set on the new basis -space is hence

defined as

ω s = ( W −1 U ) · [T n − ψ] (12)


INTERNATIONAL JOURNAL OF APPLIED BIOMEDICAL ENGINEERING VOL.2, NO.2 2009 59

6. ARTIFICIAL NEURAL NETWORK

Two-layered artificial neural network using backpropagation

training protocol was used as a classifier.

The 4x4 EMG grid data served as the input of the

neural network. The eight outputs of neural network

that correspond to the eight classes of the muscular

contraction. Structure of artificial neural network is

shown in figure 9.

Table 1: ACCURACY OF ICA AND ANN CLAS-

SIFICATION

on the muscle surface. The mapping for various pattern

of muscular contraction were then recorded and

later analyzed with independent component analysis

to classify the pattern of muscular contraction pattern.

The comparison study of classification result

demonstrates that ANN provides the comparable performance

as the ICA. Yet the ANN computational

time is noticeably less than that of the PCA.

Artificial neural network used in the classifi-

Fig.9:

cation

9. ACKNOWLEDGMENT

The authors wish to thanks ASEAN University

Network/Southeast Asia Engineering Education Development

Network (AUN/SEED-Net) to support

scholarship and The DEMAMEDICAL CO., LTD to

support the ECG/EMG surface electrode and lead

wire to measure EMG signal in this research.

7. EXPERIMENT AND RESULTS

The 15 patterns of topographical mapping of eight

muscular contraction of forearm (120 maps) were

used in the training process of ICA and used 4x4

grid (120 data) for training process of ANN. The topographical

mapping of the 15 unknown contractions

was then used as the ICA tested set. The 15 unknown

4x4 grid data was then used as ANN testd set. Figure

8 shows the ICA training sets, the derived ICA basis

and the result of ICA classification. Table 1 shows

the accuracy of ICA and ANN classification.

8. DISCUSSION AND CONCLUSION

A multi-channel electromyogram acquisition system

using PSOC microcontroller was designed and

constructed to aquire multi-channel EMG signals.The

16 EMG channels was 4x4 grid data for classification

by using artificial neural network. The 4x4 grid data

was performed to a topological map of EMG signal

References

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Antonelli, “Timing and relative intensity of hip

extensor and abductor muscle action during level


60 D. Sueaseenak et al: Muscular-Contraction Classification : Comparison Study ... (55-62)

(a) Hand close

(b) Hand open

(a)

(c) Wrist extension

(d) Wrist flexion

(e) Wrist extension

(f) Wrist flexion

(g) Radial flexion

(h) Ulnar flexion

(b)

Fig.11:

Classification results

Fig.10: (a) Training Topological Mapping Input of

ICA; (b) ICA Basis

and stair ambulation: An EMG study,” Physical

Therapy, vol. 63, pp. 1597-1605, 1983.

[7] Elaine N.Marieb., Human anatomy and physiology,

Sixth Edition, The Pearson Education; pp.

296,2004

[8] K.S. Turker, T.S Miles, “Cross talk from other

muscles can contaminate electromyographic signals

in reflex studies of the human leg,” Neuroscience

Letters, vol. 111, pp. 164-169, 1990.

[9] J.W. Morrenhof, H.J. Abbink, “Crosscorrelation

and cross talk in surface electromyography,”

Electromyography, vol. 25, pp.

73-79, 1985.

[10] Basmajian JV, de Luca CJ. Muscles Alive - The

Functions Revealed by Electromyography. The

Williams & Wilkins Company; Baltimore, 1985

[11] J. Cartinhour, “ A Bayes classifier when the class

distributions come from a common multivariate

normal distribution ” ,IEEE Transactions on Reliability,

vol. 41, Issue 2, pp. 124 - 126, 1992.

[12] N.B. Karayiannis, M.M. Randolph-Gips, “ Soft

learning vector quantization and clustering algorithms

based on non-Euclidean norms: singlenorm

algorithms ” ,IEEE Transactions on Neural

Networks, vol. 16, Issue 2, pp. 423 - 435, 2005.

[13] E. Alpaydin, M.I. Jordan, “ Local linear perceptrons

for classification ” ,IEEE Transactions on

Neural Networks, vol. 7, Issue 3, pp. 788 - 794,

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[14] Zhujie, Y.L. Yu, “ Face recognition with Eigen

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Conference on Industrial Technology, pp. 434 -

438, 1994.

[15] Comon, P., Independent component analysis; A

new concept? Signal Processing, vol 36, no. 3,

pp. 287-314,1994

[16] Cardoso, J.-F., Infomax and Maximum Likelihood

for Source Separation. IEEE Letters on

Signal Processing, vol. 4. pp. 112-114, 1997

[17] Hyvärinen, A., The Fixed-point Algorithm and

Maximum Likelihood Estimation for Independent

Component Analysis. Neural Processing

Letters, vol. 10: pp. 1-5, 1999.

[18] Hyvärinen, A. and E. Oja, Independent Component

Analysis: Algorithms and Applications.

Neural Networks, 2000. vol. 13, no. 4-5, pp. 411-

430, 2000.

[19] Bell, A.J. and T.J. Sejnowski, An informationmaximization

Approach to Blind Separation and

Blind Deconvolution. Neural Computation, vol.

7, no. 6, pp. 1129-1159, 1995

[20] Bartlett, M.S., H.M. Lades, and T.J. Sejnowski.

Independent component representations for face

recognition. in SPIE Symposium on Electronic

Imaging: Science and Technology; Conference

on Human Vision and Electronic Imaging III,

San Jose, CA, 1998

W. Iampa received the B.Sc. (Radiological

Technology) and M.Sc. (Radiological

Technology) from Mahidol

University, Thailand in 2005 and

2008, respectively. She is currently

a Ph.D. student in Department of

Electronics, Faculty of Engineering,

KMITL.

M. Sangworasil was born in Bangkok,

Thailand in 1951. He received the Bachelor

of engineering and Master o Engineering

from King Mongkut’s Institute

of Technology at Ladkrabang, Bangkok,

Thailand in 1973 and 1977 respectively,

and the D. Eng (Electronics) from Tokai

University, Japan, in 1990. Following

his graduate studies, he worked almost

28 years at Electronic De- partment,

Faculty of Engineering, King Mongkut’s

Institute of Technology at Ladkrabang, Bangkok where he is

currently an associate professor. His research interest are in the

area of image process with emphasis on Imag Reconstruction,

3D modeling, Image Classication and Image Filtering.

D. Sueaseenak received the B.Eng.

(Electrical Engineering) from Srinakharinwirot

University, Thailand, in 2005,

and the M.Eng. (Biomedical Electronics)

from King Mongkut’s Institute of

Technology Ladkrabang, Thailand, in

2007. He is currently a Ph.D. student

in Department of Electronics, Faculty of

Engineering, KMITL.

T. Chanwimalueang received the

B.Eng. (Electrical Engineering) from

Khon Kaen University, Thailand, in

2000, and the M.Eng. (Biomedical

Electronics) from King Mongkut’s Institute

of Technology Ladkrabang, Thailand,

in 2007. He is currently a Lecturer

in Biomedical Engineering programme,

Faculty of Engineering, Srinakharinwirot

University.


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