Classification of multi-lead ECG based on multiple scales and hierarchical feature convolutional neural networks

Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchical features combined with the Lead Encoder Attention (LEA) mechanism for multi-lead ECG classification. We validated the performance of our proposed method using the intrapatient approach of the MIT-BIH Arrhythmia (MIT-BIH-AR) Database and the interpatient approach of the Chinese Cardiovascular Disease Database (CCDD). Our model achieves an Accuracy (Acc) of 99.5% for the classification of normal and abnormal heartbeats in the MIT-BIH-AR database. Our method achieves a TPR95 (NPV under the condition of True Positive Rate being equal to 95 percent) of 78.5% and an Acc of 88.5% when classifying normal and abnormal ECG records from over 150,000 ECG records in the CCDD. The cross-dataset experimental results also confirm the model's strong generalization capability.