A Novel Hybrid Model Based on Convolutional Neural Network With Particle Swarm Optimization Algorithm for Classification of Cardiac Arrhythmias

Abstract

An electrocardiogram (ECG) is a non-invasive study used for the diagnosis of cardiac arrhythmias (CAs). The identification of a cardiac arrhythmia depends on its classification. This classification has been approached through different strategies, both mathematical and computational. In this work, a new computational model based on the particle swarm optimization (PSO) algorithm and convolutional neural network (CNN) is proposed for the classification of five classes of CAs obtained from the MIT-BIH Arrhythmia Dataset (MITDB). The goal of the PSO is to optimize the hyperparameters that define the layered architecture of a CNN, to increase the accuracy and decrease the categorical crossentropy error (CE). The proposed model found a satisfactory layered architecture in 17.68 hours, obtaining an accuracy of 98% and 97%, a CE of 0.044968 and 0.084768, in training and testing, respectively. These results demonstrate that the proposed model is reliable and represents an innovative approach because it allows dispensing with the manual selection of the hyperparameters of the layered architecture of a CNN.