An Improved CNNLSTM Algorithm for Automatic Detection of Arrhythmia Based on Electrocardiogram Signal

Arrhythmia is one of the most common types of cardiovascular disease and poses a significant threat to human health. An electrocardiogram (ECG) assessment is the most commonly used method for the clinical judgment of arrhythmia. Using deep learning to detect an ECG automatically can improve the speed and accuracy of such judgment. In this paper, an improved arrhythmia classification method named CNN-BiLSTM, based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), is proposed that can automatically identify four types of ECG signals: normal beat (N), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). Compared with traditional CNN and BiLSTM models, CNN-BiLSTM can extract the features and dependencies before and after data processing better to achieve a higher classification accuracy. The results presented in this paper demonstrate that an arrhythmia classification method based on CNN-BiLSTM achieves a good performance and has potential for application.