Xie Pengwei, Wang Guijin, Zhang Chenshuang, Chen Ming, Yang Huazhong, Lv Tingting, Sang Zhenhua, Zhang Ping
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。
