Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network
Myocardial Infarction Classification Based on Convolutional Neural Network and Recurrent Neural Network
复制标题
基于卷积神经网络和循环神经网络的心肌梗死分类
DOI：
10.3390/app9091879
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发表时间：
2019-05-01
影响因子：
2.7
通讯作者：
Sun, Kai
中科院分区：
文献类型：
--
作者：
Feng, Kai;Pi, Xitian;Sun, Kai
心肌梗死是对人类危害最大的心血管疾病之一。随着可穿戴设备和便携式心电图医疗设备的快速发展，及时检测和监测心肌梗死心电信号已成为可能和可能。本文提出了一种结合16层卷积神经网络（CNN）和长短期记忆网络（LSTM）的i导联心电多通道自动分类算法。该算法对原始数据进行预处理，首先提取心跳片段；然后在多通道CNN和LSTM中对其进行训练，自动学习获取的特征，完成心肌梗死心电分类。我们利用Physikalisch-Technische Bundesanstalt （PTB）数据库对算法进行验证，准确率为95.4%，灵敏度为98.2%，特异性为86.5%，F1评分为96.8%，表明该模型可以在不需要复杂手工特征的情况下获得良好的分类性能。
Myocardial infarction is one of the most threatening cardiovascular diseases for human beings. With the rapid development of wearable devices and portable electrocardiogram (ECG) medical devices, it is possible and conceivable to detect and monitor myocardial infarction ECG signals in time. This paper proposed a multi-channel automatic classification algorithm combining a 16-layer convolutional neural network (CNN) and long-short term memory network (LSTM) for I-lead myocardial infarction ECG. The algorithm preprocessed the raw data to first extract the heartbeat segments; then it was trained in the multi-channel CNN and LSTM to automatically learn the acquired features and complete the myocardial infarction ECG classification. We utilized the Physikalisch-Technische Bundesanstalt (PTB) database for algorithm verification, and obtained an accuracy rate of 95.4%, a sensitivity of 98.2%, a specificity of 86.5%, and an F1 score of 96.8%, indicating that the model can achieve good classification performance without complex handcrafted features.