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

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

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

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

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

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

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

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

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

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

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

 Hardware Methods 1210/31/2012

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

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

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

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

Methods 1710/31/2012

Methods 1810/31/2012

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

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

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

Methods 2210/31/2012

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

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

References [1] M. S. Manikandan and S. Daudapat, Quality Controlled Wavelet Compression of ECG Signals by WEDD. Los Alamitos, CA: IEEE Comput. Soc, [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 [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 [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 [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 [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 [7] [8] /31/2012

Thank You For Your Attention 2610/31/2012