Abstract
Arrhythmia is a cardiovascular disease that seriously affects human health. The identification and diagnosis of arrhythmia is an effective means of preventing most heart diseases. In this paper, a BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. Firstly, the discrete wavelet transform is used to denoise the ECG signal, based on which we performed heartbeat segmentation and preserved the timing relationship between heartbeats. Then, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. Finally, the tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. And the interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. This method divides the heartbeat into five categories (nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), fused heartbeats (F), and unknown heartbeats (Q)) and is validated on the MIT-BIH arrhythmia database. The results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper not only improves the classification accuracy and obtains higher sensitivity and positive predictive value but also has higher interpretability.
Highlights
With the improvement of people’s living standards, the incidence and mortality of cardiovascular diseases are increasing year by year and are accompanied by a younger trend [1]
Method e heartbeat classification method of the BiLSTM-Treg algorithm that integrates rhythm information between heartbeats proposed in this paper mainly includes the following steps: firstly, the data are preprocessed to filter out the noise in the ECG signal and segment ECG signal into heartbeats
Analyze the Key Nodes of the Simulated Decision Tree. e tree regularization method used in this paper looks for the decision tree representation of the model in the training process of the network. e generated decision tree simulates the decision process of the BiLSTM network model
Summary
Jinliang Yao ,1,2 Runchuan Li ,1,2 Shengya Shen, Wenzhi Zhang ,1,2 Yan Peng ,1,2 Gang Chen ,1,2 and Zongmin Wang 1,2. A BiLSTM-Treg algorithm that integrates rhythm information is proposed to realize the automatic classification of arrhythmia. En, different heartbeat segment lengths and the BiLSTM network model are used to conduct multiple experiments to select the optimal heartbeat segment length. The tree regularization method is used to optimize the BiLSTM network model to improve classification accuracy. The interpretability of the neural network model is analyzed by analyzing the simulated decision tree generated in the tree regularization method. E results show that the overall classification accuracy of the algorithm is 99.32%. Compared with other methods of classifying heartbeat, the BiLSTM-Treg network model algorithm proposed in this paper improves the classification accuracy and obtains higher sensitivity and positive predictive value and has higher interpretability
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