论文标题
心律失常分类器使用二进制卷积神经网络用于资源受限的设备
Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices
论文作者
论文摘要
监测心电图信号对于心律不齐的诊断具有重要意义。近年来，深度学习和卷积神经网络已被广泛用于心律不齐的分类。但是，应用于ECG信号检测的现有神经网络通常需要大量计算资源，这对资源受限的设备不友好，并且很难实时监控。在本文中，提出了一个适合ECG监控的二进制卷积神经网络，该网络是对硬件友好的，更适合用于资源受限的可穿戴设备。针对MIT-BIH心律失常数据库，基于该网络的分类器在五级测试中达到了95.67％的精度。与拟议的基线全精度网络相比，精度为96.45％，仅降低0.78％。重要的是，它实现了计算加速度的12.65倍，是存储压缩比的24.8倍，并且仅需要四分之一的内存开销。
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.
