测试技术学报2024，Vol.38Issue(3)：264-273,10.DOI:10.3969/j.issn.1671-7449.2024035
基于CNN-LSTM-SE的心电图分类算法研究
Study on ECG Classification Algorithm Based on CNN-LSTM-SE
摘要
Abstract
Cardiovascular disease is one of the diseases with high mortality rate in China.Monitoring elec-trocardiograms to determine if there are abnormalities in the electrical signals of the heart can be used to prevent and screen for cardiovascular disease.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 dur-ing electrocardiogram screening.In order to improve the screening efficiency of electrocardiogram and reduce the pressure on medical staff,a model based on convolutional neural network,long and short-term memory neural network and SE network(CNN-LSTM-SE)was proposed to divide electrocardiogram into five categories.The main research contents include:MIT-BIH arrhythmia data set is selected as the data source of ECG signals,Butterworth bandpass filter is used to de-noise ECG signals,Z-score method is used to standardize ECG signals,and unique thermal coding method is used to encode ECG labels.Finally,the proposed algorithm model is trained and tested using the processed ECG data.The experi-mental results show that compared with other models,the proposed model can effectively improve the accuracy of ECG classification,and the classification accuracy of the experimental data set reaches 99.1%.关键词
心律失常/心电图/卷积神经网络/SE网络/长短期记忆神经网络Key words
arrhythmia/electrocardiogram/convolutional neural network(CNN)/SE net/long and short term memroy neural network(LSTM)分类
医药卫生引用本文复制引用
王建荣,邓黎明,程伟,李国翚..基于CNN-LSTM-SE的心电图分类算法研究[J].测试技术学报,2024,38(3):264-273,10.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）
