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Arrhythmia Beat Classification Model Based on CNN and BiLSTM |
Yang Hao1, Huang Maolin1, Cai Zhipeng2, Yao Yingjia1, Li Jianqing2, Liu Chengyu2* |
1(Lenovo Research, Shenzhen 518057, Guangdong, China) 2(School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China) |
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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.
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Received: 11 March 2019
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