基于卷积长短时记忆网络的心律失常分类方法

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。

Arrhythmia Classification Based on Convolutional Long Short Term Memory Network

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.