Deep Learning Systems for the Classification of Cardiac Pathologies Using ECG Signals

Abstract

In this paper, several deep learning models are analyzed for the construction of the automated helping system to ECG classification. The methodology presented in this article begins with a study of the different alternatives for performing the discrete wavelet transform-based scalogram for an ECG. Then, several Deep Learning architectures are analysed. Due to the large number of architectures in the literature, seven have been selected as they have a high degree of acceptance in the scientific community. The influence of the number of epochs used for training will also be analysed. In addition to the development of a classifier able to accurately solve the multi-class problem of, given an ECG signal, deciding which pathology the subject is suffering from (main interest for a medical expert), we also want to rigorously analyze, through the use of a statistical tool (ANOVA), the impact the main functional blocks of our system have on its behaviour. As a novel result of this article, different homogeneous groups of deep learning systems are analysed (from a statistical point of view, they have the same impact on the accuracy of the system). As can be seen in the results, there are four homogeneous groups, with the group with the lowest accuracy index obtaining an average value of 76,48% in the classification and the group with the best results, with an average accuracy of 83,83%.