自动化与信息工程2024，Vol.45Issue(4)：18-23,6.DOI:10.3969/j.issn.1674-2605.2024.04.003
基于卷积注意力的单导联心电图房颤检测方法
Single Lead Electrocardiogram Atrial Fibrillation Detection Method Based on Convolutional Attention
摘要
Abstract
With the popularity of wearable electrocardiographic devices,the method of automatically detecting atrial fibrillation from single lead electrocardiograms is becoming increasingly important.A residual neural network model Resnet34-CAB based on convolutional attention is proposed to address the issue of noise interference in single lead electrocardiograms collected by wearable electrocardiographic devices.By integrating Convolutional Attention Blocks(CAB),the detection performance of the model is improved by selectively focusing on key features of the electrocardiogram and adaptively suppressing noise,with a small increase in model complexity.The experimental results on public datasets show that the Resnet34 CAB model outperforms the Resnet34 and Resnet34 Transformer models,verifying the effectiveness of the fusion CAB.关键词
单导联心电图/卷积注意力块/房颤检测/残差神经网络Key words
single lead electrocardiogram/convolutional attention block/atrial fibrillation detection/residual neural network分类
信息技术与安全科学引用本文复制引用
丘荣建,王剑卓..基于卷积注意力的单导联心电图房颤检测方法[J].自动化与信息工程,2024,45(4):18-23,6.基金项目
广东省基础与应用基础研究基金(2022A1515011445) （2022A1515011445）
