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the next signal package has been arrived. In the PDA case, for processing cycles of one second, this property is not achieved. Nevertheless, good results are obtained for processing cycles of two seconds. As we have explained before, the package size has a direct influence on the rhythm delay detection, and therefore, in the alarm detection. The average of the time necessary to detect the rhythm for this type of packages appears in figure 9. Figure 9: Rhythm detection delay For packages of one second the PC achieves an average of 4.43 seconds in obtaining the rhythm, meanwhile the PDA needs 10.3 seconds, but, as the processing cannot be performed in real time, the rhythm detection delay would grow if the experiment duration were longer. For packages of two seconds the PC obtains an average of 5.2s to detect the rhythm meanwhile the PDA needs approximate 6.66s to detect it. Notice that we have also experimented with signal packages of 3 seconds and seen that real-time processing is also possible. But, as the rhythm delay time is greater than with signal packages of 2 seconds, so we conclude that the entire ECG analysis process should be performed in the PDA every 2 seconds. Finally, an explanation of the rhythm detection delays obtained is because the detection algorithm used needs the previous beat types and the next three ones, in order to detect the rhythm for the current beat. 4.2 Alarm Notification: Cost and Latency The aim of this subsection is to compare the MOLEC system, which only sends alarm signals to the medical center (according to the Alarm Manager Module policy described in section 3), with a system that continuously sends the ECG signals picked up from the sensors and detects the alarm in the medical A WIRELESS APPLICATION THAT MONITORS ECG SIGNALS ON-LINE center. Supposing that a GPRS network is used for communication, we present further the communication costs for the alarm notification during 30 minutes of monitoring. For this test we used three ECG records: the 100 record that does not contain any alarm, so there is no communication with the medical center; the 207 record that is from a patient with serious health problems and contains five minutes of alarms; and 124 record that is from a typical user, so it contains an average notifications amount. All the ECG signals used have the same sampling frequency (360 samples/second). For the system that sends all the signals we supposed that each sample has 2 bytes. In the next figure it can be observed the amount of data that the system would send in each case. Figure 10: Communication costs 273 A system, that does not make any processing and compressing of the signal, would send approximately 1296Kb for each 30 minutes which means more than 2Mb/hour, meanwhile the MOLEC-PDA-Holter, in the worst case would send more than 100 times less. If no abnormalities are detected then there is no communication with the health center, as in the case of the record 100. Therefore the amount of communication with the health center is drastically reduced, and so the communication costs. Another variable that should be taken into account is the delay between the time when the risk episode starts and the moment when the health center notices it. The time needed for the MOLEC system to notify an alarm is tnotify = trhythm detection+talarm compression + tcommunication latency In other words the notification delay depends on the time needed to detect the rhythm in the PDA, the time needed to compress the alarm message and the latency that the GPRS network involves in order to send it to the monitor center. On the other hand, in the case of the systems that continuously send ECG signals to the medical center the notification delay would depend only on the
274 ENTERPRISE INFORMATION SYSTEMS VI communication delay over the GPRS network and the rhythm detection time in the medical center. As it could be observed in the figure 9, the delay of the rhythm detection, with our algorithm, is greater in the PDA (around seven seconds) than in a normal PC with only one user (around four seconds). In a system with more users that continuously send ECG signals the costs to obtain the same results would be greater. Moreover, a system that continuously sends ECG signals to a monitor center would send many, but small signal packages through the network, what means, if the connection is stable, a constant latency in the communication; meanwhile the MOLEC system sends compressed alarm messages from time to time but with a bigger amount which suppose a bigger latency to send it. Therefore, the notification latency is a few seconds bigger in the MOLEC system but still remains in a reasonable threshold giving the possibility to the user to obtain medical assistance in time. 5 CONCLUSIONS In this paper we have presented the global architecture of an innovative system called MOLEC that allows an on-line monitoring of people suffering from heart arrhythmias. Among the advantages of that system are the following ones: 1) Promptness: MOLEC detects anomalous rhythms, anywhere and anytime, as soon as they are produced, and sends the corresponding alarm to the hospital; and 2) Efficiency: MOLEC optimizes the use of wireless communications and PDA resources. In order to achieve those advantages, we have designed and performed some experiments that consisted in calculating the rate of the processing cycle that the system can tolerate in order to be efficient, stable and the rhythm detection delay minimal. That time has been 2 seconds in the case of the PDAs. Special attention has also been paid in minimizing the cost of the wireless communications without increasing the delay time for the detected serious heart anomalies. That can be achieved by performing the ECG signal processing and rhythm classification locally in the PDA and by sending only alarms to the hospital. REFERENCES Bluetooth. 2003. www.bluetooth.com Cardio Control.2003. www.cardiocontrol.com/cardio.htm Daja, N., Relgin, I., Reljin B., 2001. Telemonitoring in Cardiology –ECG transmission by Mobile Phone. Annals of the Academy of Studenica 4, 2001. Despopoulos, A.., Silbernagl, S. 1994, Texto y Atlas de fisiología. ISBN: 84-8174-040-3. Dimitri Konstansas Val Jones, Rainer Hersog. 2003. MobiHealth- innovative 2.5/3G mobile services and applications for healthcare. Workshop on Standardization in E-Health. Geneva, Italy. Eclipse 2003. http://www.eclipse.org/. Farreras and Rozman, “Medicina interna”. Decimatercera edición. Edición en CD-ROM. Sección 3. Cardiologia pag 395 – 523. October, 2001. GNU 2003. http://gcc.gnu.org/java/ Handhelds 2003. http://www.handhelds.org/. Health Level 7 (HL7). 2003. http://www.hl7.org/. Jané, P., Blasi, A., García, J., Laguna, P. 1997. Evaluation of an Automatic Threshold Based Detector of Waveform Limits in Holter ECG with the QT database”. Computers in Cardiology, vol. 24, pp. 295- 298. Kunze, C., Gromann, U., Stork, W., Müller-Glaser, K.D.,2002. Application of Ubiquitous Computing in Personal Health Monitoring Systems. 36. annual meeting of the German Society for Biomedical Engineering. Le Blanc, R., “Quantitative analysis of cardiac arrhythmias.” CRC: Critical Review in Biomedical engineeering, 14(1):1-43, 1986 MIT-BIH Database Distribution. 2003. http://ecg.mit.edu/ Mitchell, T.M., “Machine Learning.” ISBN 0-07-042807- 7. Section 3: Decision tree learning. Pages 52-75. Pan, J., Tompkin, W. J. 1985. A real-time QRS detection algorithm". IEEE Trans. Biom. Eng. BME-32: 230- 236. Rodríguez, J., Goñi A., Illarramendi, A. 2003. Classifying ECG in an On-Line Monitoring System. Submitted for Publication. Rodríguez, J., Goñi A., Illarramendi, A. Capturing, Analyzing and Managing ECG Sensors Data in Handheld Devices. DOA 2003. Sachpazidis 2002. @Home: A modular telemedicine system. Mobile Computing in Medicine, Proceedings of the 2. Workshop on mobile computing. Heidelberg, Germany, 2002. Ventracor Limited. 2003 http://www.ventracor.com
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vi TABLE OF CONTENTS PART 1 - DATAB
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viii TABLE OF CONTENTS A WIRELESS A
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CONFERENCE COMMITTEE Honorary Presi
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Hung, P. (AUSTRALIA) Jahankhani, H.
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Invited Papers
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MANAGING COMPLEXITY OF ENTERPRISE I
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Hit Rate (%) ACME-DB: AN ADAPTIVE C
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ACKNOWLEDGEMENTS We would like to t
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Data Rate [bytes/sec] 1600000 14000
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PART 2 Artificial Intelligence and
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Abdelkafi, N. .....................
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