测试技术学报2024，Vol.38Issue(2)：161-169,9.DOI:10.3969/j.issn.1671-7449.2024.02.009
基于多特征分支卷积神经网络的心电图分类算法
Electrocardiogram Classification Algorithm Based on Multi Feature Branch Convolutional Neural Network
摘要
Abstract
The incidence rate and mortality of cardiovascular diseases in China are increasing year by year.However,due to the large scale and complexity of electrocardiogram data,clinical medical staff have a heavy workload and are prone to misdiagnosis or missed diagnosis during electrocardiogram screen-ing.Based on this,in this paper we proposes an intelligent classification and analysis of multi-lead electro-cardiogram signals based on multi feature branch convolutional neural networks using CPSC-2018 twelve lead data.Firstly,divide the CPSC-2018 12-lead data into 9 categories,derive 8-lead electrocardiogram signals based on the 12 leads,and extract local features separately.Then,the attention weight vectors of different categories are calculated through bidirectional GRU encoding and attention mechanism,and the feature information is concatenated and fused into feature vectors to achieve multi-lead electrocardiogram classification.The experimental results showed that good classification performance was achieved on the validation set,with an F1 value of 81.2%for normal categories and an average F1 value of 84.2%.Espe-cially,when identifying two types of arrhythmia,atrial fibrillation(AF)and right bundle branch block(RBBB),F1 values reached 95.1%and 93.1%,respectively.关键词
心律失常/心电图/卷积神经网络/GRU网络/注意力机制Key words
arrhythmias/electrocardiogram/convolutional neural network/GRU network/attention mechanism分类
医药卫生引用本文复制引用
王建荣,程伟,邓黎明,李国翚..基于多特征分支卷积神经网络的心电图分类算法[J].测试技术学报,2024,38(2):161-169,9.基金项目
国家重点研发计划资助项目(2018YFC2000701) （2018YFC2000701）
中国博士后科学基金资助项目(2021M692400) （2021M692400）
山西省基础研究计划资助项目(202203021221017) （202203021221017）
