用于心电图心跳分类的双向递归神经网络和卷积神经网络（BiRCNN）
Bidirectional Recurrent Neural Network And Convolutional Neural Network (BiRCNN) For ECG Beat Classification.
作者信息
Xie Pengwei, Wang Guijin, Zhang Chenshuang, Chen Ming, Yang Huazhong, Lv Tingting, Sang Zhenhua, Zhang Ping
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2555-2558. doi: 10.1109/EMBC.2018.8512752.
We propose a novel electrocardiogram (ECG) beat classification algorithm using a combination of Bidirectional Recurrent Neural Network (BiRNN) and Convolutional Neural Network (CNN) named as BiRCNN. Our model is an end-to-end model. The morphological features of each ECG beat is extracted by CNN. Then the features of each beat are considered in the context via BiRNN. The assessment on MIT-BIH Arrhythmia Database (MITDB) resulted in a sensitivity of 98.7% and a positive predictivity of 96.4% on average for the VEB class. For the SVEB class, the sensitivity was 92.8%, which was an over 6% promotion compared with the state-of-the-art method, and the positive predictivity was 81.9% on average. The results demonstrate the superior classification performance of our method.
我们提出了一种新颖的心电图（ECG）搏动分类算法，该算法结合了双向循环神经网络（BiRNN）和卷积神经网络（CNN），名为BiRCNN。我们的模型是一个端到端模型。每个心电图搏动的形态特征由CNN提取。然后，通过BiRNN在上下文环境中考虑每个搏动的特征。对麻省理工学院-比哈尔心律失常数据库（MITDB）的评估结果显示，对于室性早搏（VEB）类别，平均灵敏度为98.7%，阳性预测值为96.4%。对于室上性早搏（SVEB）类别，灵敏度为92.8%，与现有最先进方法相比提高了6%以上，平均阳性预测值为81.9%。结果证明了我们方法具有卓越的分类性能。
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