基于改进残差网络对心电信号的识别

Recognition of ECG Signal Based on Modified Residual Network

心血管疾病是当今人类死亡的主要原因之一。本文基于改进的残差网络对心电信号进行识别,并将改进后的残差网络和空洞卷积进行结合,特征提取时保持局部信息不变的同时尽可能地提取全局信息。研究使用K折交叉验证对MIT⁃BIH心律失常数据集进行训练、验证和测试。首先使用卷积层汇集输入图像,其次利用改进后的网络进行特征提取,最后使用Softmax分类器进行分类。在MIT⁃BIH心律不齐数据库中,提出的模型在没有任何额外人工特征和数据增强进行辅助的情况下,获得了97.20%的准确度、92.85%的敏感度、98.29%的特异性、93.16%的精确度和93.00%的F1分数。该研究将为医疗机构对于心电信号检测识别提供技术支撑,从而减轻专业医师的工作负荷。

Cardiovascular disease is one of the main causes of human death.Based on the modified residual network,we identify ECG signals and combine the modified residual network with dilated convolution to extract global information as much as possible while keeping local information unchanged in feature extraction.The MIT-BIH arrhythmia data set is trained,validated and tested using K-fold cross validation.In the experiment,firstly,the convolution layer is used to collect the input images.Secondly,the modified network is used to extract the features.Finally,the Softmax classifier is used for classification.In the MITBIH arrhythmia database,the proposed model achieves 97.20%accuracy,92.85%sensitivity,98.29%specificity,93.16%accuracy and 93.00%F1 score without any additional artificial features and data augmentation.This research will provide technical support for the detection and recognition of ECG signals to reduce the workload of professional doctors in medical institutions.