Weighted Dual-Channel Model for Arrhythmia Detection using Single Electrocardiogram Signal

Abstract

The single-lead method plays a central role in arrhythmia warning, and its anomaly detection is of practical significance for the prevention of heart diseases. However, the detection performance is often limited due to the limited information content of single-lead electrocardiogram (ECG) signals. To address this issue, this paper proposes a weighted dualchannel arrhythmia classification model aimed at improving the detection performance of single-lead ECG signals. The model employs a parallel structure of CNN-LSTM and Transformer to capture both local and global features of the ECG signals. By applying weighted learning to the features from the two channels, the model achieves precise arrhythmia classification. Additionally, to alleviate the class imbalance problem, a weighted cross-entropy loss function is introduced, further enhancing the classification performance. Experimental results based on the MIT-BIH arrhythmia database demonstrate that the proposed model exhibits excellent performance in terms of overall accuracy, F1 score, and sensitivity, achieving 99.33%, 94.71%, and 99.33%, respectively. Compared with existing classification models, the proposed model shows superior classification performance and significant practical value, providing solid technical support for the auxiliary diagnosis of arrhythmias and bringing new possibilities for actual clinical applications.