Presenter : Shao-Kai Liao

Adviser : Tsung-Fu Chien

Chairman : Hung-Chi Yang

Date : 5.22.201315/22/2013

OutlineOutline

Paper ReviewPaper Review

Purpose

IntroductionIntroduction

MethodsMethods

ConclusionsConclusions

Future WorkFuture Work

ReferencesReferences

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Paper ReviewPaper Review

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Input-Feature Correlated Asynchronous Analog to Information Converter for ECG MonitoringRitika Agarwal, Student Member, IEEE, and Sameer R. Sonkusale, Member, IEEE

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(a) Example of a synchronously sampled signal.

(b) Example of an adaptive asynchronously sampled signal modeled after our prior approach

Paper ReviewPaper Review

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

IntroductionIntroduction

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Electrocardiogram (ECG)

P waveatrial contraction

QRS complexventricular contraction

T waverepolarisation of the ventricles

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IntroductionIntroduction Wireless ECG signal transmission system

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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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MethodsMethods

Software TinyOS platform

AVR Studio 4

NesC

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Hardware

MethodsMethods

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ZigbeX Mote

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Hardware

MethodsMethods

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Wireless ECG signal transmission system5/22/2013

Hardware

MethodsMethods

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Biomedical remote home care wireless sensor BIO module patch position

The measured ECG signals

Hardware

MethodsMethods

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Nurse Auto Calling System UD-885

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Software ECG asynchronous sampling

MethodsMethods

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ECG asynchronous sampling trigger physiological signal high / low threshold

Conclusions

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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 WorkFuture Work

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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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