Presenter : Shao-Kai Liao Adviser : Tsung-Fu Chien Chairman : Hung-Chi Yang Date : /22/2013

Outline  Paper Review  Purpose  Introduction  Methods  Conclusions  Future Work  References 25/22/2013

Paper Review 3 Input-Feature Correlated Asynchronous Analog to Information Converter for ECG Monitoring Ritika Agarwal, Student Member, IEEE, and Sameer R. Sonkusale, Member, IEEE 5/22/2013 (a) Example of a synchronously sampled signal. (b) Example of an adaptive asynchronously sampled signal modeled after our prior approach

Paper Review 4 5/22/2013 Dotted line: input ECG signal. Bold line: input-feature-correlated asynchronously taken samples.

Introduction 5  Electrocardiogram (ECG) P wave atrial contraction QRS complex ventricular contraction T wave repolarisation of the ventricles 5/22/2013

Introduction  Wireless ECG signal transmission system 6 Wireless ECG signal transmission system 5/22/2013

Purpose  Reduce the burden of the nurses caring for patients.  Monitor environmental information for each ward.  Immediately notify the nurse at physiological signal abnormalities. 75/22/2013

Methods  Software TinyOS platform AVR Studio 4 NesC 85/22/2013

 Hardware Methods 9 ZigbeX Mote 5/22/2013

 Hardware Methods 10 Wireless ECG signal transmission system 5/22/2013

 Hardware Methods 1110/31/2012 Biomedical remote home care wireless sensor BIO module patch position The measured ECG signals

 Hardware Methods 12 Nurse Auto Calling System UD-885 5/22/2013

 Software  ECG asynchronous sampling Methods 3/7/ ECG asynchronous sampling trigger physiological signal high / low threshold

Conclusions 14  Highly efficient to bring a revolutionary change in ambulatory health monitoring.  Make emergency room abnormal physiological signals machine noise reduction.  reduce the number of wireless signal through asynchronous sampling algorithm 5/22/2013

Future Work 15  Detect the P, Q, R, S and T waves.  Collected from the raw data is stored to the SD card is easy to observe when the error occurred  Integrated ECG physiological signal monitoring in the nurse call system. 5/22/2013

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 /22/2013

Thank You For Your Attention 175/22/2013