Input-Feature Correlated Asynchronous Analog to Information

Converter for ECG Monitoring

Ritika Agarwal , Student Member ,IEEE , and Sameer R. Sonkusale , Member ,IEEEE

IEEE TRANSACTION ON BIOMEDICAL CIRCUITS AND SYSTEMS , VOL.5, NO. 5, OCTOBER 2011

學 生:莊凱強授課老師:王明賢

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Outline

Abstract Introduction

Motive Method Algorithm for the feature extraction

Experiments Conclusion References

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Abstract An architectural design of a novel variable intput

feature correlated asynchronous sampling and time-encode digitization approach for source compression and direct feature extraction from physiological signals.

The complete architecture represents an analog-to-information(A2I) converter ,design for ultra-low-power mixed-signal very-large-scale integrated implementation.

Simulation results show large source compression in ECG signal and more than 98% efficiency in the detection of the Q、 R and S wave for challenging ECG waveforms , all with extremely low-power and storage requirements.2015/5/19

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Introduction-Motive With the growing trend toward wearable health monitoring systems, a large amount of data is continuously collected, stored, transmitted, and processed to extract essential information from different physiological signals. These requirement prove to a big constraint for mobile or ambulatory applications where low power consumption is prerequisite. System which can compress the number of data samples collected right at the source while simultaneously capturing the main features of the signal will significantly reduce the burden on power and storage requirements.The goal is to provide early warnings to physician in case of any ectopic heartbeat in order to provide effective timely diagnosis and care to the heart patients.2015/5/19

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Introduction-Method An adaptive asynchronous sampling approach samples the input signals base on the slope, and the digital values are generated every time the signal crosses the predefined thresholds set by the built-in quantizer. The thresholds are adaptively adjusted according to the activity level of the input signal. When the signal is sparse or has low levels of activity, the signal is sampled at maximum resolution of the quantizer. However, when the input signal exhibits higher levels of activity, the quantization levels are skipped, producing less sampling point and allowing power to be saved In Fig.1(b),we show an adaptive asynchronously sampled base on the delay-mode processing approach.

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Introduction-Method Although it is an excellent compression mechanism, it could miss certain key aspects of signal. For feature extraction from any signal, the slope transition points or the peak/troughs of the signal are very critical. We further expand upon the adaptive asynchronous technique by utilizing it not just for reduction of the number of samples acquired but to enable direct detection and capture of the critical points in the waveform. We call this approach an”input-feature correlated asynchronous A2I convention”,it can be understood from Fig.2(c).

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Fig.2.(a) Example of a synchronously sampled signal.

Fig.2.(b) Example of an adaptive asynchronously sampled modeled after our prior approach.

Fig.2.(c) Example of an input-feature correlated asynchronously sampled signal.

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Introduction-Algorithm for the feature extration

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Basically, if Dout(n-2)<Dout(n-1) and Dout(n-1)>Dout(n);the feature extraction block recognizes the occurrence of a slope transition.

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Introduction-Algorithm for the feature extration

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The same algorithm is followed for the calculation of trough.These peak and trough heights obtained then are used for the calculation of the top and the buttom thresholds for adaptive technique

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Experiments

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Fig.(d) Asynchronous sampling apporach

Fig.(e) synchronous sampling

apporach

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Conclusion The design of input-feature-correlated A2I converter is proposed for the extration of relevant information and critical feature from the input signal right at the sensor output. The system consumes very low power and is void of all complexities. 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.

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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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