Abstract
Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat classification. Firstly, the noise is removed by the continuous wavelet transform method. Secondly, the peak of the R wave is detected by the tagged database, and then the P-QRS-T wave morphology and the RR interval are extracted. This feature set is heartbeat activity's global sequence features, which combines single heartbeat morphology and 21 consecutive RR intervals. Finally, the Bi-LSTM algorithm and the BiLSTM-Attention algorithm are used to identify heartbeat category respectively, and the MIT-BIH arrhythmia database is used to verify the algorithm. The results show that the BiLSTM-Attention model combined with heartbeat activity's global sequence features has higher interpretability than other methods discussed in this paper.
Highlights
In recent years, with the improvement of people’s living standards, the prevalence of cardiovascular diseases has increased significantly
The BiLSTM-Attention deep neural network hybrid model designed in this paper considers the information above and below the heartbeat and the key position of the heartbeat
This paper focus on the three main steps: preprocessed data, P-QRS-T wave group extraction, RR interval calculation, and heartbeat classification
Summary
With the improvement of people’s living standards, the prevalence of cardiovascular diseases has increased significantly. A new explanatory deep learning method is used for heartbeat classification, which based on heartbeat activity’s sequence features and BiLSTM-Attention neural network. The contributions of this work are as follows: 1.In the case of unbalanced ECG data set, a novel neural network learning algorithm based on BiLSTM-Attention model is proposed for heartbeat classification. 3. The BiLSTM-Attention deep neural network model and heartbeat activity’s global sequence features are used to classify various kinds of arrhythmias of different patients. Acharya et al [23] developed a 9-layer deep convolutional neural network to automatically identify five different categories of heartbeats in ECG signals. ECG diagnosis algorithm based on deep learning can identify and judge the arrhythmia event more effectively It is important for modern medical treatment. Where B is the training data, C is the number of ECG heartbeat categories, b means a beat, pc (b) is the probability of predicting b as class c given by the softmax layer, and pc(b) indicates whether class c is the correct ECG heartbeat category, whose value is 1 or 0
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