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 : 3.7.201213/7/2012

OutlineOutline IntroductionIntroduction

Purpose

Methods & Methods & MaterialsMaterials

Results

ConclusionsConclusions

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IntroductionIntroduction 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 )

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IntroductionIntroduction

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Electrocardiogram (ECG)

P waveatrial contraction

QRS complexventricular contraction

T waverepolarisation of the ventricles

IntroductionIntroduction

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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.

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Methods & Methods & MaterialsMaterials

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(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 & Methods & MaterialsMaterials

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Architecture of the proposed A2I converter.

Methods & Methods & MaterialsMaterials

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(b) Detection of a trough.

(a). Detection of a peak.

Methods & Methods & MaterialsMaterials

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Dotted line: input ECG signal. Bold line: input-feature-correlated asynchronously taken samples.

Methods & Methods & MaterialsMaterials

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QRS detection algorithm.

QRS detection.

Methods & Methods & MaterialsMaterials

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QRS detection in the ECG signal based on sampled data points.

Results

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Results

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Results

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Results

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Conclusions

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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, 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.

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Thank You For Your Attention

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