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 : /7/2012

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

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

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

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.

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

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

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

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

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

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

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

Results 3/7/201213

Results 3/7/201214

Results 3/7/201215

Results 3/7/201216

Conclusions 3/7/  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.

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 /7/201218

Thank You For Your Attention 3/7/201219