王丹妮,
杜强,
姜楚迪
沈阳工业大学电气工程学院 沈阳 110870
基金项目:国家自然科学基金(51377109)，辽宁省自然科学基金(2019-ZD-0204)
详细信息
作者简介:柯丽：女，1977年生，博士，教授，博士生导师，研究方向为生物电工与阻抗成像技术
王丹妮：女，1995年生，硕士生，研究方向为医学信号处理与分析
杜强：男，1975年生，博士，讲师，研究方向为生物医学信号检测与处理
姜楚迪：女，1996年生，硕士生，研究方向为医学电磁工程及医疗仪器
通讯作者:柯丽 keli@sut.edu.cn
中图分类号:TP391; R540.41计量
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被引次数:0
出版历程
收稿日期:2019-09-16
修回日期:2020-02-20
网络出版日期:2020-03-23
刊出日期:2020-08-18
Arrhythmia Classification Based on Convolutional Long Short Term Memory Network
Li KE,,Danni WANG,
Qiang DU,
Chudi JIANG
School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Funds:The National Natural Science Foundation of China (51377109), The Natural Science Foundation of Liaoning Province (2019-ZD-0204)
摘要
摘要:心律失常等慢性心血管疾病严重影响人类健康，采用心电信号(ECG)实现心律失常自动分类可有效提高该类疾病的诊断效率，降低人工成本。为此，该文基于1维心电信号，提出一种改进的长短时记忆网络(LSTM)方法实现心律失常自动分类。该方法首先设计深层卷积神经网络(CNN)对心电信号进行深度编码，提取心电信号形态特征。其次，搭建长短时记忆分类网络实现基于心电信号特征的心律失常自动分类。基于MIT-BIH心律失常数据库进行的实验结果表明，该方法显著缩短分类时间，并获得超过99.2%的分类准确率，灵敏度等评价参数均得到不同程度的提高，满足心电信号自动分类实时高效的要求。
关键词:心电信号/
心律失常/
深度学习/
卷积神经网络/
长短时记忆网络
Abstract:Chronic cardiovascular diseases such as arrhythmia seriously affect human health. The automatic classification of ElectroCardioGram(ECG) signals can effectively improve the diagnostic efficiency of such diseases and reduce labor costs. To tackle this problem, an improved Long-Short Term Memory (LSTM) method is proposed to achieve automatic classification of one dimensional ECG signals. Firstly, deep Convolutional Neural Network (CNN) is designed to deeply encode the ECG signal, and ECG signal morphological features are extracted. Secondly, the LSTM classification network is used to realize automatic classification of arrhythmia of ECG signal features. Experimental studies based on the MIT-BIH arrhythmia database show that the training duration is significantly shortened and more than 99.2% classification accuracy is obtained. Sensitivity and other evaluation parameters are improved to meet the real-time and efficient requirements for automatic classification of ECG signals.
Key words:ElectroCardioGram (ECG) signal/
Arrhythmia/
Deep learning/
Convolutional Neural Network (CNN)/
Long Short-Term Memory (LSTM)
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