Presenter : Shao-Kai Liao
Adviser : Tsung-Fu Chien
Chairman : Hung-Chi Yang
Date : 5.22.2013
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Outline

Paper Review

Purpose

Introduction

Methods

Conclusions

Future Work

References
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Paper Review
(a) Example of a synchronously sampled signal.
(b) Example of an adaptive asynchronously sampled signal
modeled after our prior approach
Input-Feature Correlated Asynchronous Analog to Information Converter for ECG Monitoring
Ritika Agarwal, Student Member, IEEE, and Sameer R. Sonkusale, Member, IEEE
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Paper Review
Dotted line: input ECG signal.
Bold line: input-feature-correlated asynchronously taken samples.
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Introduction
 Electrocardiogram
(ECG)
P wave
T wave
atrial contraction
repolarisation of the ventricles
QRS complex
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ventricular contraction
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Introduction

Wireless ECG signal transmission system
Wireless ECG signal transmission system
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Purpose

Reduce the burden of the nurses caring for patients.

Monitor environmental information for each ward.

Immediately notify the nurse at physiological signal abnormalities.
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Methods
 Software

TinyOS platform

AVR Studio 4

NesC
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Methods
 Hardware
ZigbeX Mote
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Methods
 Hardware
Wireless ECG signal transmission system
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Methods
 Hardware
10/31/2012
The measured
ECG
Biomedical
remote home
caresignals
wireless sensor
BIO module patch position
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Methods
 Hardware
Nurse Auto Calling System UD-885
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Methods

Software

ECG asynchronous sampling
ECG asynchronous sampling trigger
physiological signal high / low threshold
3/7/2012
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Conclusions

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
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Future Work

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.
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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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