A Portable Tele-Emergent System With ECG Discrimination in SCAN Devices

1. National Taipei University of Technology Computer and Communication Engineering A Portable Tele-Emergent System With ECG Discrimination in SCAN Devices Speaker：Ren-Guey LeeDate ：2004 Auguest 25 B.E. LAB

2. Outline • Introduction • System Functions • System Architecture • QRS Detection Algorithm • ECG Discrimination Algorithm • Results and Conclusion • References B.E. LAB

3. Introduction

4. Introduction • ECG provides information of condition of heart. • The system concept that under existing GSM communication system using SMS and Tele-emergence device. • Features of the device are light, compact and wireless. B.E. LAB

5. System function • Tele-emergence systemintegrates : • ECG signals acquisition circuit. • ECG discrimination technology. • Sensor Network technology. • GSM communication system. • Bluetooth communication technology. • GPS position service. B.E. LAB

6. System Architecture B.E. LAB

7. QRS Detection Algorithm Most automatic ECG diagnosis require an accurate detection of the QRS complexes. B.E. LAB

8. continue QRS Detection Algorithm • The QRS Detection algorithm : • “Tompkins” method. • “So and Chan” method. • “Modified So and Chan” method is based on “So and Chan” and “Tompkins” QRS detection algorithms. B.E. LAB

9. continue QRS Detection Algorithm • Low-pass filter： • Cut-off frequency：12 Hz • Delay：5 points • Gain：36 B.E. LAB

10. continue QRS Detection Algorithm • High-pass filter： • Cut-off frequency：5 Hz • Delay：16 points • Gain：32 B.E. LAB

11. continue QRS Detection Algorithm • The slope of the ECG wave is obtained by : • Let X(n) represent the amplitude of the ECG data at discrete time n. B.E. LAB

12. continue QRS Detection Algorithm • The slope threshold is given by : • The thresh_param can set as 2,4,8,16. • The initial maxi is the maximum slope within the first 250 data points in the ECG file. B.E. LAB

13. continue QRS Detection Algorithm • Detection QRS onset have two case : 1. (Set Max = True) 2. (Set Max = False) • When two consecutive ECG data satisfy above condition, the QRS onset point has been detected. B.E. LAB

14. continue QRS Detection Algorithm • Maxi is then updated by • The filter_param can be set as 2,4,8,16. B.E. LAB

15. continue QRS Detection Algorithm

16. MIT-BIH ECG database

17. QRS Detection Algorithm(2)

18. ECG Discrimination Algorithm • ECG Discrimination technology is based on : • QRS detection algorithm. • Geometric correlation coefficient.

19. continue ECG Discrimination Algorithm • Heart Rate Variability (HRV) formula: B.E. LAB

20. continue ECG Discrimination Algorithm • Correlation Coefficient : • n : size of the sample points • xi : Template • yi : Sample • mx,xy : mean value B.E. LAB

21. continue ECG Discrimination Algorithm HRV and Correlation coefficient (Record 119)

22. continue ECG Discrimination Algorithm ECG template (Record 119)

23. continue Results and Conclusion On average, the FD% of the “Modified So and Chan” method is 1.11 % while “So and Chan” method is 5.47%. (MIT-BIH Database 48 records) B.E. LAB

24. continue Results and Conclusion • Affected ECG discrimination accuracy factors： • QRS Detection accuracy. • ECG Template created. • Threshold parameter selected. • Noise interference • ECG baseline wander B.E. LAB

25. continue Results and Conclusion • User integration device has six parts : • ECG acquisition circuit. • Bluetooth module. • GPS module. • GSM module. • Touch panel. • MSP 430. B.E. LAB

26. continue Results and Conclusion Plot the ECG wave in PC Sending ECG wave in terms of ASCI code from SCAN device

27. Next steps and Problems Implement R-wave detection and Correlation Coefficient in SCAN device Time complexity of algorithm must be too high when implementing Correlation Coefficient May find other methods suitable for sensor network B.E. LAB

28. continue Next steps and Problems • Power-saving issue should be considered • From routing protocol ? • From MAC protocol ? • Collision problems • Overhearing problems • Control package overhead problems • Idle listening problems

29. References • P. Jiapu and W. J. Tompkins., “A Real-Time QRS Detection Algorithm,” IEEE trans. on bio-medical engineering, Vol. 32, No. 3, pp. 230-236, March 1985. • G. M. Friesen, T. C. Jannett, et al., “A comparison of the noise sensitivity of nine QRS detection algorithms,” IEEE Trans. on Biomedical Engineering, Vol. 37, pp. 85- 98, Jane 1990. • K. F. Tan, K. L. Chan and K. Choi, “Detection of the QRS complex, P wave and T wave in electrocardiogram, “Processing of 2000 IEE Conference on Advances in Medical Signal and Information Processing, pp. 41-47, Sept 2000. • H.H. So and K.L. Chan, “Development of QRS detection method for real-time ambulatory cardiac monitor,” Proceedings of the 19th Annual International Conference of the IEEE in Engineering in Medicine and Biology society, Vol. 1, Oct. 1997, pp. 289-292.

30. References continue • H. A. N. Dinh, D. K. Kumar, et al., “Wavelets for QRS Detection,” Engineering in Medicine and Biology Society Proceedings of the 23rd Annual International Conference of the IEEE, Vol. 2,, pp. 1883-1887, Oct. 2001. • K. T. Lai and K. L. Chan, ”Real-time classification of electrocardiogram based on fractal and correlation analyses,” Proceedings of the 20th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society, Vol. 1 pp. 119-122, 1998. • Wei Ye et al., “Medium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks” IEEE/ACM TRANSACTIONS ON NETWORKING, VOL.12, NO.3, JUNE 2004 • Soo-Hwan Choi et al., “An Implementation of Wireless Sensor Network for Security System using Bluetooth” IEEE Transactions on , Vol. 50, No. 1, February 2004