Low-Power Online Ecg Analysis Using Neural Networks Publisher



Modarressi M^{1, 2} ; Yasoubi A¹ ; Modarressi M^{1, 2}
Source: Proceedings - 19th Euromicro Conference on Digital System Design# DSD 2016 Published:2016

Abstract

Digital healthcare devices have attracted many attentions in recent years. In particular, the cardiovascular system is always an important target of such digital healthcare devices. In this paper, we present a neural network-based low-power online ECG analysis and monitoring system. Electrocardiogram (ECG) is a representation of the heart's electrical activity recorded from electrodes on the body surface and serves as an important source of assessing heart's function. The proposed low-power design is based on the observation that a considerable amount of computations in neural network-based ECG monitoring is repetitive and can be eliminated to save power. We identify two sources of computation redundancy: the input data (which have relatively little change, as long as the heart functions properly) and neuron weights. By exploiting computation reuse to eliminate redundant computation the proposed architecture can achieve high power-efficiency for online ECG analysis which is crucial for an implant or wearable healthcare device. Experimental results on some MIT-BIH database ECG waveforms show that the proposed mechanism can reduce ECG analysis power consumption by 45%. © 2016 IEEE.