融合CNN和BiLSTM的心律失常心拍分类模型

doi:10.3969/j.issn.0258-8021.2020.06.009

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Abstract In order to extract the abnormal beats more accurately from the dynamic electrocardiograph (ECG), a deep learning model combining convolutional neural network (CNN) and bi-directional long short-term memory network (BiLSTM) was proposed in this study. Firstly, ECG signals were segmented into two types of time window lengths: a small-scale length of 0.75 s and a large-scale length of 4 s. Then, features were extracted from the small- and large-scale length ECG segments using an 11-layer CNN network and a 3-layer BiLSTM network, respectively. Finally, the extracted features were combined and were then reduced using a 3-layer fully connected network. In addition, two data enhancement methods by adding random motion noise and baseline drift were used to attenuate the influence of over-fitting due to the unbalanced data distribution. The proposed model was tested on the MIT arrhythmia database using a patient-based 5-fold cross-validation method, and its accuracy for classifying the 4 types (normal, atrial premature, ventricular premature and unclassified) on 116,000 heartbeats was 90.42%, which was 13.97% and 7.14% higher than the CNN model (76.45%) and BiLSTM model (83.28%), respectively. This study validated that the proposed model with combining CNN and BiLSTM reports higher accuracy than only using CNN or BiLSTM model when performing the abnormal beat classification task.

Key words： arrhythmia beat classification electrocardiograms (ECG) convolutional neural network (CNN) bi-directional long short-term memory network (BiLSTM)

Received: 11 March 2019

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R318

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	Articles by authors
	Yang Hao
	Huang Maolin
	Cai Zhipeng
	Yao Yingjia
	Li Jianqing
	Liu Chengyu

Cite this article:

Yang Hao,Huang Maolin,Cai Zhipeng, et al. Arrhythmia Beat Classification Model Based on CNN and BiLSTM[J]. Chinese Journal of Biomedical Engineering, 2020, 39(6): 719-726.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.06.009 OR http://cjbme.csbme.org/EN/Y2020/V39/I6/719

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