1. Ritika Agarwal, Student Member, IEEE, and Sameer R. Sonkusale, Member, IEEE,” Input-Feature Correlated Asynchronous Analog to Information Converter for ECG Monitoring” IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 5, NO. 5, OCTOBER 2011 Chairman : Hung-Chi Yang Presenter : Shao-Kai Liao Adviser : Tsung-Fu Chien Date : 3.7.2012 13/7/2012

2. Outline  Introduction  Purpose  Methods & Materials  Results  Conclusions 3/7/20122

3. Introduction  Electrocardiogram (ECG) A important diagnostic tool for Medicine. Measure the electrical activity of the heart. Provides valuable information about the functioning of the heart and basically the entire cardiovascular system.  心電圖（ Electrocardiogram, ECG ) 3/7/20123

4. Introduction 4  Electrocardiogram (ECG) P wave atrial contraction QRS complex ventricular contraction T wave repolarisation of the ventricles

5. Introduction 3/7/2012  Electrocardiogram (ECG) 5 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.

6. Purpose  For wireless body sensor applications, the reconstruction can be performed after the data has been transmitted to an external receiver to save power.  This can provide early warnings to the physician for the patient’s condition. 3/7/20126

7. Methods & Materials 3/7/20127 (a) Example of a synchronously sampled signal. (b) Example of an adaptive asynchronously sampled signal modeled after our prior approach (c) Example of an input-feature correlated asynchronously sampled signa

8. Methods & Materials 3/7/20128 Architecture of the proposed A2I converter.

9. Methods & Materials 3/7/20129 (b) Detection of a trough. (a). Detection of a peak.

10. Methods & Materials 3/7/201210 Dotted line: input ECG signal. Bold line: input-feature-correlated asynchronously taken samples.

11. Methods & Materials 3/7/201211 QRS detection algorithm. QRS detection.

12. Methods & Materials 3/7/201212 QRS detection in the ECG signal based on sampled data points.

13. Results 3/7/201213

14. Results 3/7/201214

15. Results 3/7/201215

16. Results 3/7/201216

17. Conclusions 3/7/201217  The adaptive samples taken from an ECG signal can be processed to detect the Q, R, and S waves.  An adaptive asynchronous sampling technique generates roughly 40% less samples than the regular asynchronous sampling technique.  The whole system is highly efficient and can bring a revolutionary change to today’s world where ambulatory health monitoring is the demand of the era.

18. 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. 3/7/201218

19. Thank You For Your Attention 3/7/201219