Multi-classification of arrhythmias using ResNet with CBAM on CWGAN-GP augmented ECG Gramian Angular Summation Field

Abstract

Cardiovascular diseases are the leading cause of death globally. Arrhythmias are the most common symptoms and can cause sudden cardiac death. Accurate and reliable detection of arrhythmias from large amount of ECG signals remains a challenge. We here propose to use ResNet with convolutional block attention modules (CBAM-ResNet) to classify the major types of cardiac arrhythmias. To facilitate the classifier in extracting the rich information in the ECG signals, we transform the time series into Gramian angular summation field (GASF) images. In order to overcome the imbalanced data problem, we employ the conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) model to augment the minor categories. Tested using the MIT-BIH arrhythmia database, our method shows classification accuracy of 99.23%, average precision of 99.13%, sensitivity of 97.50%, specificity of 99.81% and the average F1 score of 98.29%. Compared with the performance of the state-of-the-art algorithms in the extant literature, our method is highest accuracy and specificity, comparable in precision, sensitivity and F1 score. These results suggest that transforming the ECG time series into GASF images is a valid approach to representing the rich ECG features for arrhythmia classification, and that CWGAN-GP based data augmentation provides effective solution to the imbalanced data problem and helps CBAM-ResNet to achieve excellent classification performance.