1. Presenter : Shao-Kai Liao Adviser : Tsung-Fu Chien Chairman : Hung-Chi Yang Date : 10.31.2012 110/31/2012

2. Outline  Paper Review  Introduction  Purpose  Methods  Conclusions  Future Work  References 10/31/20122

3. Paper Review  Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms 310/31/2012

4. Paper Review  Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms 410/31/2012

5. Paper Review  Adaptive Beat-to-Beat Heart Rate Estimation in Ballistocardiograms 510/31/2012

6. Introduction 6  Electrocardiogram (ECG) P wave atrial contraction QRS complex ventricular contraction T wave repolarisation of the ventricles 10/31/2012

7. Introduction  Electrocardiogram (ECG) 7 R-R Interval QRS complex is the most significant feature of the ECG signal. R-R interval is the time distance between the two consecutive R waves is used to detect any irregularity in the normal working of the heart. 10/31/2012

8. Introduction  Wireless ECG signal transmission system 8 Wireless ECG signal transmission system

9. Purpose  Reduce the burden of the nurses caring for patients.  Monitor environmental information for each ward.  Immediately notify the nurse at physiological signal abnormalities. 910/31/2012

10. Methods  Software TinyOS platform AVR Studio 4 NesC 1010/31/2012

11.  Hardware Methods 1110/31/2012 Biomedical remote home care wireless sensor BIO module ZigbeX Mote

12.  Hardware Methods 1210/31/2012

13.  Hardware Methods 1310/31/2012 Wireless ECG signal transmission system

14.  Hardware Methods 1410/31/2012 Biomedical remote home care wireless sensor BIO module patch position The measured ECG signals

15. Methods 15 (b) Detection of a trough. (a). Detection of a peak. 10/31/2012

16. Methods 16 QRS detection algorithm. QRS detection. 10/31/2012

17. Methods 1710/31/2012

18. Methods 1810/31/2012

19. Methods 1910/31/2012 MIT/BIT record 100. Reduced from the original 3600 points to 517 points

20. Methods 2010/31/2012 MIT/BIT record 101. Reduced from the original 3600 points to 452 points

21. Methods 2110/31/2012 MIT/BIT record 103. Reduced from the original 3600 points to 419 points

22. Methods 2210/31/2012

23. Conclusions 23  The adaptive samples taken from an ECG signal can be processed to detect the Q, R, and S waves.  Highly efficient to bring a revolutionary change in ambulatory health monitoring.  Make emergency room abnormal physiological signals machine noise reduction. 10/31/2012

24. Future Work 24  RR interval analysis and detect RR interval abnormalities.  Integrated ECG physiological signal monitoring in the nurse call system. 10/31/2012

25. References [1] M. S. Manikandan and S. Daudapat, Quality Controlled Wavelet Compression of ECG Signals by WEDD. Los Alamitos, CA: IEEE Comput. Soc, 2007. [2] L. Zhitao, K. Dong Youn, and W. A. Pearlman, “Wavelet compression of ECG signals by the set partitioning in hierarchical trees algorithm,” IEEE Trans. Biomed. Eng., vol. 47, no. 7, pp. 849–856, Jul. 2000. [3] E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Tran˙s. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006. [4] E. J. Candes and M. B. Wakin, “An introduction to compressive sampling,” IEEE Signal Process. Mag., vol. 25, no. 2, pp. 21–30, Mar. 2008. [5] E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: Universal encoding strategies?,” IEEE Trans. Inf. Theory, vol. 52, no. 12, pp. 5406– 5425, Dec. 2006. [6] M. F. Duarte, M. A. Davenport, D. Takhar, J. N. Laska, S. Ting, K. F. Kelly, and R. G. Baraniuk, “Single-pixel imaging via compressive sampling,” IEEE Signal Process. Mag.,, vol. 25, no. 2, pp. 83–91, Mar. 2008. [7] http://www.uniphone.com.tw/tw/products/call_ud885.php?SelectLeftStyle=3http://www.uniphone.com.tw/tw/products/call_ud885.php?SelectLeftStyle=3 [8] http://physionet.ph.biu.ac.il/http://physionet.ph.biu.ac.il/ 2510/31/2012

26. Thank You For Your Attention 2610/31/2012