Automated arrhythmia classification using depthwise separable convolutional neural network with focal loss

Arrhythmia was one of the primary causes of morbidity and mortality among cardiac patients. Early diagnosis was essential in providing intervention for patients suffering from cardiac arrhythmia. Convolution neural network (CNN) was widely used for electrocardiogram (ECG) classification. However, the conventional CNN method only worked well for balanced dataset. Therefore, a depthwise separable convolutional neural network with focal loss (DSC-FL-CNN) method was proposed for automated arrhythmia classification with imbalance ECG dataset. The focal loss contributed to improving the arrhythmia classification performances with imbalance dataset, especially for those arrhythmias with small samples. Meanwhile, the DSC-FL-CNN could reduce the number of parameters. The model was trained on the MIT-BIH arrhythmia database and it evaluated the performance of 17 categories of arrhythmia classification. Comparing with state-of-the-art methods, the experimental results showed that the proposed model reached an overall macro average F1-score with 0.79, which achieved an improvement for arrhythmia classification.