Bidirectional Recurrent Neural Network And Convolutional Neural Network (BiRCNN) For ECG Beat Classification

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

doi:10.1109/EMBC.2018.8512752

Bidirectional Recurrent Neural Network And Convolutional Neural Network (BiRCNN) For ECG Beat Classification

Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul:2018:2555-2558. doi: 10.1109/EMBC.2018.8512752.

Authors

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

PMID: 30440929
DOI: 10.1109/EMBC.2018.8512752

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Arrhythmias, Cardiac
Electrocardiography*
Humans
Neural Networks, Computer
Signal Processing, Computer-Assisted