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

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

3 Introduction Introduction

4 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. Introduction B.E. LAB

5 System function Tele-emergence system integrates : 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 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. continue B.E. LAB

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

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

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

12 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. QRS Detection Algorithm continue B.E. LAB

13 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. QRS Detection Algorithm continue B.E. LAB

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

15 QRS Detection Algorithm continue

16 MIT-BIH ECG database

17 QRS Detection Algorithm(2)

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

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

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

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

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

23 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) Results and Conclusion continue B.E. LAB

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

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

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

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 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 continue

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 , March 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 , Jane 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 , Sept 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

30 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 , Oct 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 , 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 , Wei YeMedium Access Control With Coordinated Adaptive Sleeping for Wireless Sensor Networks 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 An Implementation of Wireless Sensor Network for Security System using Bluetooth 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 continue References