Electronic Presentation Guidelines - ICMCC

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Electronic Presentation Guidelines - ICMCC

ICMCC Event 2005. e-Health Symp. Part 4

Hybrid and customized approach in

telemedicine systems: an unavoidable

destination

Manuel Prado, Laura M. Roa, Javier Reina-Tosina

Biomedical Engineering Group, University of Seville

Escuela Superior de Ingenieros

Seville, (Spain) 41092

1


Outline

• New methodological approach for tele-healthcare

systems

• Aim: generation of clinical and physiological

knowledge

–Real time

– Customization capabilities

• Patient Physiological Image (PPI)

• Three key issues are evaluated:

– Capability of the PPI to determine internal state of

patient

– Physical activity monitor following this

methodology

– Knowledge integration at different scales

2


Motivation: healthcare challenges

• Population aging

• Growth of chronic pathologies

• Change of social models and increase of quality of

life

• Accelerated growth in economical expenditure of

current centralized healthcare

• Proved limitation in the outcomes of many therapies

related to a lack of customization taking into account

the overall pathophysiology of the patient

• Need of a deeper synergy between biomedicine

advances (genomic, proteomic and upper levels) and

clinical areas

3


Motivation: current THC/TM systems

• Telehealthcare systems are presented as a new

paradigm pursuing the decentralization of healthcare,

but …

• … scarce clinical implantation

• Mainly oriented to multimedia services based on

advanced telecommunications networks and

enhanced monitoring

• Barriers regarding their effective and massive

implantation are primarily due to inertial and policyrelated

constraints

4


Rationale

• Telecommunication services offer a large enough

number of connectivity solutions able to support

modern telemedicine services

• There is a explosion of advances in wearable,

minimally invasive biosensors and monitors

• The role of international standards in telemedicine

• Database methodologies and technologies are

adequate to support the complexity of signals

patterns, objects and functions that are needed in

telemedicine systems

• Good outcomes in cost-effectiveness

5


Rationale (II)

• Convergence and overlap of functions in TM systems

• Management of patient’s clinical information:

knowledge-based telehealthcare

• Lack of methodologies and technologies that allow to

extract useful medical information in a real time mode

from on-line biosignals

6


Objectives

• Methodological approach for generation of clinical and

physiological knowledge of the user in real-time

• Knowledge generation method: Patient Physiological Image (PPI)

• A very efficient customization of the supervision, using a

distributed and hybrid computational architecture to process the

information, founded on three key issues:

– Its ability to compute personalized knowledge will be evaluated

by means of a simulation experiment over a PPI’s prototype.

– The ability of the sensor layer to process biosignals on a

customized manner within a distributed processing

architecture.

– The capability of PPI to generate knowledge from the

dynamics mathematical models that allow the integration of

physiological information pertaining to different scales, from

genomic and proteomic level to organ and systemic level.

7


Target study groups

• Chronic patients

– End Stage Renal Disease, home

hemodialysis (HD)

– Current home HD assistance oriented to

alarm telemonitoring

• Elderly population

– 20-40 % report some inability to be alone

– Quality of life

– Fear of falling and their effects

– Portable monitor for falling detection

enabling movement analysis

8


Methodological approach

• Common telehealthcare:

– Physicians acquire

knowledge about patient

health state by datadriven

models: rulebased

systems, neural

networks, statistical

models, fuzzy

algorithms, or other

signal filters.

• Our approach:

– Knowledge creation is

improved adding a new

signal processing layer

based on PPI.

PPI

RAU

Data-driven

Knowledge creator

Data-driven

Knowledge creator

Monitored variables

RAU

Patient’s RAUs

Health Center Home environment

9


Methodological approach

• Three major processing layers:

• Upper layer: clinical decision

support which can be

implemented by an expert

system. Uses data generated

by an intermediate layer.

• Intermediate layer: based on

PPIs, which are responsible for

generating a dynamics

mathematical image of several

aspects of the state of the

patient.

• Bottom layer: a sensors layer

gets an processes relevant

information from the patient and

associated therapy devices

10


Patient Physiological Image

• PPI as a computational

component: distribution,

modularity, and independence

of platform and languages

• Implemented at different levels:

– First level: XML-based

codification

– Second level: components

coded in non-causal objectoriented

modelling

language -> platformdependence

– Third level: C++ class

generation and link to other

classes and libraries ->

machine-dependence

XML- based

mathematical

model

Natural

Transformation

Non-causal

language &

object-oriented

components

(EL source)

Model generation

(Partition EL)

Algorithm-based

model

(C++ Class)

BC

PPI

architecture

Model

Equations

Components

Classes aggregation &

Object-oriented wrapper

PPI class

PPI’s model class

Execution control class

Data source classes

Mathematical Model

Tunable

Parameters

Snapshot (IC)

Execution Control

Plug-in Interfaces

Object-oriented

control &

interfaces

(C++ Classes)

Linking

PPI program

11


Physical activity monitor

Elderly

Chronic Renal disease

Human movement

monitoring

Diabetes mellitus

Parkinson

Multimorbidity

Post-stroke hemiplegia

Chronic Obstructive

Pulmonary disease

……

Personalized

telehealthcare

13


Material and methods

• Assessment of a customized supervision

• Wearable and customizable technology for

physical activity monitoring

• Integrating knowledge at different physiological

scales

14


Assessment of a customized

supervision

• Digital simulation experiment over the

mathematical model of a PPI

• Objective: validation of the capability of

the PPI to determine the internal state of

an ESRD patient during an HD session

• Basis:

• Pharmacokinetic model oriented to the

description of the urea kinetic into the

patient distribution volume.

• Implemented by a pharmacokinetic library

developed with EcosimPro language.

• Urea concentrations and related dialyzer

Kt/V (dKt/V) are compared to those

estimated by a validated two-pool kinetic

model recently published.

15


Results

• Urea concentrations obtained by

the two-pool model and by a PPI

for the selected patient. The lower

graph shows the evolution of the

compartmental volumes computed

by the PPI model.

• Urea dynamics computed by the

three-pool model-based PPI

accurately agrees with that of the

reference model

• It can be observed that the

interstice behaves as a buffer,

moderating the loss of vascular

volume and therefore reducing the

risk of hypotension events,

whereas the cellular compartment

did not contribute at all. This

behaviour is in agreement with

measurements reported in recent

studies.

16


Physical activity monitoring

• Intelligent accelerometer unit (IAU):

– 2 biaxial capacitive accelerometers (ADXL202E),

PIC16LC66 microcontroller, EEPROM and integrated

transceiver

– Processing capacity for attending requests from PSE

and for customizing a small-signal analysis

• Different strategies to read accelerations. Assessment

performed in terms of precision and power consumption

– Parallel acquisition (4 channels): minimum power

consumption, but dispersion of measurements > 5 %

– Sequential acquisition: dispersion dropped < 4 %,

power consumption still low

17


Falling warning algorithm

4

∑( i

AND

i)

i=

1

r≥

N

⎡ ⎤( , )

r = af ff

af = ⎣a > A⎦

t t + t

i i h


⎛E

⎞⎤

HP,

i

ffi = ⎢( EHP,

i

> EMIN) AND > FMIN ⎥( t, t+

th)

,


⎜ E ⎟


⎝ AC,

i ⎠⎥⎦

E ( n) = E ( n− 1) + a ( n) − a ( n−τ

)

F, i F, i F, i F,

i

• The amplitude and frequency flags, af i and ff i , are kept raised during the temporal

window t h .

• Values of thresholds N, A, E MIN , and F MIN together with the width of the temporal

windows, t h , are computed in a customized manner depending on the user and

environment, with the aim of avoiding the loss of any falling, minimizing false warnings

towards PSE.

• Nominal sampling frequency of acceleration signals was set on 40 S/s. These are resampled

to 10 S/s before to be sent to PSE

18


Evaluation procedure

• Study carried out over eight

young volunteers

• Five controlled physical

activities without impact

and six controlled physical

activities with impact

• The study was organized in

two stages:

– Non-customized mode

– Customized mode

• A1: Slow walking (hard floor)

• A2: Normal walking (hard floor)

• A3: Fast walking (hard floor)

• A4: Going upstairs (hard floor)

• A5: Going downstairs (hard floor)

• A6: vertical jump over soft floor

• A7: vertical jump over hard floor

• A8: knee falling on soft floor

• A9: knee falling on hard floor

• A10: horizontal falling from a low

bank (50 cm) over soft floor

• A11: horizontal falling from a low

bank (50 cm) over hard floor

19


Impact detection with default thresholds

Subject W H Y a 1

a 2

a 3

a 4

a 5

a 6

a 7

a 8

a 9

a 10

a 11

1 60 1.63 25 I I I I I I fx1

fx2

fx3

I I I

2 56 1.54 31 ax1

I

ax1

I I I I fx1

I

I

fx2

ex2

fx2

fx3

fx4

3 53 1.63 26 ax1

I ax1 I ax1

I

fx1

I I fx1

fx2

fx2

fx2

fx2

fx3

fx3

fx3

fx4

fx4

ax4

4 86 1.77 27 I I I I I I I I I I

5 72 1.83 26 I I ax1 I I I I I I I

6 83 1.79 23 I I I I I I I I I

7 60 1.67 23 ax1

fx3

I I ax1

fx2

I I I I I I I

8 49 1.57 26 ax1 I ax1 I I I I I I I

20


Impact detection with optimal

thresholds on soft floor

Subject a 1

a 2

a 3

a 4

a 5

a 6

a 8

a 10

1 fx1

ax1

ax1

ax1

ax1

I I I

fx2

fx3

fx1

fx3

fx1

fx3

fx3

fx2

fx4

fx3

fx4

2 ax1 ax1 ax1 ax1 I I I

3 ax2 I I I I I

4 ax1

ax2

ax1 ax1 I I I

5 fx4 I I I I I

6 fx1

fx4

ax1 I I I

7 I I I I I

8 ax1 fx3 ax1

fx3

I I I

21


Impact detection with optimal

thresholds on hard floor

Subject a 1

a 2

a 3

a 4

a 5

a 7

a 9

a 11

1 fx1

ax1

ax1

fx1

ax1 I I I

fx2

fx1

fx3

fx3

fx2

fx4

fx4

fx3

fx4

2 ax1 I I I

3 ax1 ax1 ax1 I I I

4 ax1 I I I

5 ax1 ax1

ax2

fx4

I I I

6 fx1

fx4

ax1 I I I

7 fx2 ax1 ax1 I I I

8 ax1

fx2

ax1 I I I

22


Knowledge integration by virtual

prototyping

• Virtual component: a mathematical submodel that can be linked with

other submodels by means of its connection ports

• Hierarchical structure associated to a virtual component representing

kidney collecting duct epithelium

– Two major physical processes: Lp and Lp*

• It is possible to increase the complexity or modify the behaviour of a

virtual component by simply aggregating simulation components

representing physical processes in the lower layers

23


Conclusion

• New methodological approach in THC systems

• Capability of the PPI for building an image of the internal state of the patient:

– Outcomes in agreement with those obtained by a validated reference

mathematical model

• In order to yield a practical implementation of this methodology keeping a

good cost-efficiency rate, the processing capacity related to the sensor layer is

another important issue.

– We have developed a wearable monitor that meets with this requirement

– The anthropometric characteristics of voluntaries and the type of floor

(environment) exert a strong influence on the optimal parameters

(thresholds) of the falling detection algorithm

– False falling warnings are discarded by the PSE, which constitutes the

second processing sub-layer of the monitor

• Approach to integrate multiscale knowledge into the mathematical model of

the PPI using a simplistic virtual component of the kidney collecting duct

membrane

• The goal is not decentralization but the personalization of healthcare

assistance

24


25

Renal THC system for home HD

Windows NT/2000

Active PPI

Physiologica l Model

(Visual C++ from EcosimPro)

Execution C ontrol

1 Windows NT/2000

2

CDS

6

Active PPI

H ealth state

Analyzer

Data-driven

kn owledge creator

Physiologica l Mode l

(Visual C++ from EcosimPro)

Execution C ontrol

ORB

Connectivity

Da ta Source

COM

SQL

O RB Clie nt

IIOP

SQL

Prop

Connectivity

Data Source

ORB COM SQL

LAN or VPN (2)

IIOP DCOM Prop

IIOP DCOM Prop

Data processin g

(PL/pgSQL)

Linux

Assistance DB

Processing Server

Data processing

unit 2

Linux

3

D ata processing

unit 1

Starting

Watching and

Supervision

Data pr ocessin g

(PL/pgSQL)

Trial DB

Linu x

ORDBMS instance

(PostgreSQL for Linux)

4

SCADA

ORDBMS instance

(PostgreSQL for Linux)

5

ORB

IIOP

Skel

RMI

SQL

Prop

SQL

Prop

Signal computing

Signal preprocessing

Alarm generation

and manage ment Local term

for SCADA

operation

Historic and

(X Windows)

event recorder

ORB

IIOP

ORB

IIOP

Skel

RMI

SQL

Prop

WWW server

(Apache)

IIOP

8

RA Us

ISDN

PSTN

GSM

10

Gateway

to X.25

X.25

IP

IP

Network

Node

(DPN/PP)

X.25

Communicatio ns

Server

LAN or VPN ( 1)

Prop

7

HTTP

Web Navigator

(DHTML/Script/XML)

JAVA

ActiveX

HTTP

9

CIPA

Client application

Connectivity

Data Source

ORB SQL

Prop

Asynchronous

tra nsmission

INTERNET

Stub

RMI

ORB

IIOP

COM

D COM

SQL

Prop


Monitor description

WPAN

WIRELESS C.

RAU

PRT

IAU

PSE

y

WAN

z

m

x

y

CELLULAR

TELEPHONE

Median Plane

PROCESSING CENTER

26


1.6

c(g/l)

1.2

0.8

0.4

1.6

c(g/l)

1.2

0.8

0.4

0.718

0.639

0.607

0.544

15

1.58

c(t) i

c(t) v

c(t) v

eKt/V=0.895

c(t) c

Time(min) 240 270

(a)

1.58

c(t) i

eKt/V=1.080

c(t) c

Time (min) 240 270

(c)

Results

1.6

c(g/l)

1.2

0.8

0.4

1

0.625

0.543

15

1.58

c(t) i

c(t) v

2

First trial

Second trial

eKt/V=1.048

c(t) c

Time(min) 240 270

(b)

Therapy adjusting

t

PPI ev. without ther. adjust.

Last trial

(d)

Patient evolution

Target point

PPI evolution

t

27

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