基于CNN-LSTM 混合模型的心律失常自动检测

doi:10.6040/j.issn.1672-3961.0.2020.445

摘要/Abstract

摘要： 提出一种卷积神经网络(convolutional neural network, CNN)和长短时记忆(long short-term memory, LSTM)网络混合的心律失常自动检测算法,模型结构共由5层卷积层、5层池化层、1层LSTM层和1层全连接层组成。利用CNN能够自动提取特征和LSTM能够捕捉时间序列前后依赖关系的能力,将简单预处理后的心电信号数据直接输入到混合模型当中。整个模型将特征提取和分类器分类2个步骤结合,从而更加高效、准确地识别5种不同的心律失常疾病。在测试集上进行试验,准确率、敏感性和特异性分别为99.48%、99.47%和99.86%。试验结果表明,本研究提出的方法能够高效、准确地识别不同类型的心律失常疾病。

关键词: 卷积神经网络, 长短时记忆网络, 自动检测, 心电图, 心律失常

Abstract: A hybrid algorithm of convolutional neural network and long short-term memory network was proposed for automatic detection of arrhythmias. The model structure was composed of 5 convolutional layers, 5 pooled layers, 1 LSTM layer and 1 fully connected layer. By taking advantage of CNN's ability to automatically extract features and LSTM's ability to capture dependencies before and after time series, the simple preprocessed ECG signal data were directly input into the hybrid model. The whole model combined the two steps of feature extraction and classifier classification, so as to identify five different arrhythmias more efficiently and accurately. The accuracy, sensitivity and specificity of the test set were 99.48%, 99.47% and 99.86% respectively. The experimental results showed that the proposed method could efficiently and accurately identify different types of arrhythmias.

Key words: CNN, LSTM, automatic detection, electrocardiogram, arrhythmia

中图分类号:

TP391

陶亮,刘宝宁,梁玮. 基于CNN-LSTM 混合模型的心律失常自动检测[J]. 山东大学学报 (工学版), 2021, 51(3): 30-36.

TAO Liang, LIU Baoning, LIANG Wei. Automatic detection research of arrhythmia based on CNN-LSTM hybrid model[J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 30-36.

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