Multi-Scale and Multi-Channel Information Fusion for Exercise Electrocardiogram Feature Extraction and Classification

Abstract

Increased physical activity can help reduce the occurrence of cardiovascular disease. However, cardiovascular disease during strenuous exercise also brings certain risks, so a convenient and effective method is needed to accurately identify heart rate. Due to the low amplitude characteristics of ECG signals, automatic classification of the imperceptibility and irregularities of ECG signals remains a great challenge. To address this issue, we propose an automatic heart rate detection method using a two-dimensional convolutional neural network. First, the ECG data is preprocessed to convert the multi-lead single-channel ECG data into dual-channel ECG data. Then, the ECG image is input into the convolutional neural network. To enhance the diagnostic accuracy of an abnormal heart rate diagnosis model, this study integrates a multi-scale pyramid module into the network to fully extract image contextual information. Experimental analysis utilizes MIT-BIH and Sudden Cardiac Death Holter public datasets for training and testing. The results show that the final classification accuracy reaches 99.12% and 98.40%, respectively. The method requires no manual pre-processing of converted ECG images, has high accuracy, can effectively monitor abnormal heart rate, and minimize sudden death caused by abnormal heart rate.

Keywords

• ECG signal
• multi-scale
• deep learning
• convolutional neural network
• information fusion