ECG Arrhythmia Classification Using Recurrence Plot and ResNet-18

Abstract

Cardiovascular diseases are the leading cause of death worldwide, claiming approximately 17.9 million lives each year. In this study, a novel CAD system to detect and classify electrocardiogram (ECG) signals is presented. Designed system employs the recurrence plot (RP) approach that transforms a ECG signal into a 2D representative colour image, finally performing their classifications via employment of Deep Learning architecture (ResNet-18). Novel system includes two steps, where the first step is the preprocessing one, which performs segmentation of the data into two-second intervals, finally forming images via the RP approach; following, in the second step, the RP images are classified by the ResNet- 18 network. The proposed method is evaluated on the MIT-BIH arrhythmia database where 5 principal types of arrhythmias that have medical relevance should be classified. Novel system can classify the before-mentioned quantity of diseases according to the AAMI Standard and appears to demonstrate good performance in terms of criteria: overall accuracy of 97.62%, precision of 95.42%, recall of 95.42%, F1-Score of 95.06%, and AUC of 95.7% that are competitive with better state-of-the-art systems. Additionally. the method demonstrated the ability in mitigating the problem of imbalanced samples.