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

Outline  Abstract  Introduction  Motive  Method  Algorithm for the feature extraction  Experiments  Conclusion  References 2015/5/192

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. 2015/5/193

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 4

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. 2015/5/195

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). 2015/5/196

7 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.

Introduction-Algorithm for the feature extration 2015/5/198 Basically, if Dout(n-2) Dout(n);the feature extraction block recognizes the occurrence of a slope transition.

Introduction-Algorithm for the feature extration 2015/5/199 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

Experiments 2015/5/1910 Fig.(d) Asynchronous sampling apporach Fig.(e) synchronous sampling apporach

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. 2015/5/1911

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