Arrhythmia Classifier Using a Layer-wise Quantized Convolutional Neural Network for Resource-Constrained Devices

Abstract

An arrhythmia diagnosis neural network can perform real-time diagnosis through continuous monitoring, and it can warn against potential risks. Moreover, these networks can be installed in resources-constrained devices like wearable devices. However, the existing neural networks suffer from high memory consumption and power consumption, which limit their application in low-power resources-constrained devices. Here, we proposed a novel neural network classifier to classify 17 different rhythm classes using 1,000 long-duration electrocardiograms, achieving a classification accuracy of 95.72%, which is 4.32% higher than current state-of-the-art methods. Additionally, we proposed a layer-wise quantization method based on the greedy algorithm and compared it to other quantization methods. The proposed classifier achieved a 95.39% classification accuracy and reduced memory consumption by 15.5 times. Our study realizes a neural network with high performance and low resources consumption, and it demonstrates the possibility of implementing neural networks in resources-constrained devices for continuous monitoring, real-time diagnosis, and potential risk warnings.