Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices

TL;DR

This paper introduces a binarized convolutional neural network tailored for ECG arrhythmia classification on resource-limited devices, achieving high accuracy with significantly reduced computational and memory requirements.

Contribution

The paper presents a novel binarized CNN architecture optimized for ECG monitoring on resource-constrained wearable devices, balancing accuracy and efficiency.

Findings

Achieved 95.67% accuracy on MIT-BIH dataset.

Reduced memory overhead to a quarter of full-precision networks.

Provided 12.65x speedup and 24.8x storage compression.

Abstract

Monitoring electrocardiogram signals is of great significance for the diagnosis of arrhythmias. In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias. However, the existing neural network applied to ECG signal detection usually requires a lot of computing resources, which is not friendlyF to resource-constrained equipment, and it is difficult to realize real-time monitoring. In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices. Targeting the MIT-BIH arrhythmia database, the classifier based on this network reached an accuracy of 95.67% in the five-class test. Compared with the proposed baseline full-precision network with an accuracy of 96.45%, it is only 0.78% lower. Importantly, it achieves 12.65 times the computing speedup, 24.8 times the storage compression ratio, and only requires a quarter of the memory overhead.