计算机应用与软件2023，Vol.40Issue(12)：209-216,8.DOI:10.3969/j.issn.1000-386x.2023.12.031
结合注意力机制的CNN-LSTM心电信号识别
ECG SIGNAL RECOGNITION BASED ON CNN-LSTM CONBINED WITH ATTENTION MECHANISM
张锐 1曾鑫1
作者信息
- 1. 哈尔滨理工大学自动化学院 黑龙江 哈尔滨 150080
- 折叠
摘要
Abstract
The complexity and diversity of ECG signal forms easily lead to low recognition accuracy and poor adaptability.It's time-consuming,laborious and costly to rely on ECG experts to participate in feature recognition.Therefore,a new deep network model,the A-CNN-LSTM,is proposed that combined with the attention mechanism,long short-term memory and convolutional neural network.This model was built on convolutional neural network,where the attention mechanism was introduced after the pooling layer of the convolutional neural network to help extracting spatial features of the ECG signal.The temporal feature within them could be captured by LSTM to be used in classification.The experiment was conducted on the MIT-BIH arrhythmia database.The experimental results show that this model can classify six different kinds of ECG signals and have achieved a classification accuracy of 99.23%.The model is of certain application significance clinically.关键词
卷积神经网络/长短时记忆网络/注意力机制/心电信号识别Key words
Convolutional neural network/Long short-term memory/Attention mechanism/ECG signal recognition分类
信息技术与安全科学引用本文复制引用
张锐,曾鑫..结合注意力机制的CNN-LSTM心电信号识别[J].计算机应用与软件,2023,40(12):209-216,8.
