Multi-Channel Lightweight Convolution Neural Network for Anterior Myocardial Infarction Detection

Myocardial Infarction (MI) is one of the major causes of human death. The electrocardiogram (ECG) is an important tool of the myocardial infarction diagnosis. In this paper, we propose the Multi-Channel Lightweight Convolutional Neural Network (MCL-CNN), which combines ECG signals from three leads (V1, V2, and V3) for the purpose of detecting the Anterior Myocardial Infarction (AMI). MCL-CNN utilizes the signal from each ECG lead in order to find the suitable filter to get the superior feature representation. On one hand, MCL-CNN uses the squeeze convolution, the depthwise convolution, and the pointwise convolution to extract ECG features, which has lower computational complexity than the traditional CNN. On the other hand, Adam optimizer is applied by MCL-CNN to improve the classification performance. Comparing to both the single-channel convolutional neural network and the multi-channel convolutional neural networks, experimental results show that MCL-CNN can achieve a superior metrics scores (i.e., Accuracy=96.18%, AUC=95.50%, Sensitivity=93.67%, Specificity=97.32%). Experimental results also demonstrate the MCLCNN's rationality of multi-lead ECG classification and the lower computational cost.