Capsule Network Based on Scalograms of Electrocardiogram for Myocardial Infarction Classification

Abstract

Myocardial infarction (MI) is one of the leading causes of mortality throughout the world. Early diagnosis of MI is crucial for effective treatment to avoid patient morality. In this regard, the most commonly used technique for the problem of MI detection is the Convolutional Neural Network (CNN), which has shown good performance, but it still has some limitations. CNN requires a large amount of data, which is a challenge in the medical field. Therefore, the proposed approach uses a novel architecture consisting of wavelet transform and Capsule network, which is the most advanced algorithm to overcome CNN’s drawback. Experimental results achieve an accuracy of 91.2%, Sensitivity of 83% and Specificity of 89.5% which demonstrates that CapsNet acquires promising results while using fewer data.