ECG Signal Classification based on combined CNN Features and Optimised Support Vector Machine

Abstract

The electrocardiogram (ECG) is a visual depiction of the electrical activity of the heart. It is utilised to detect and diagnose different cardiac conditions. Over the last decade, the categorization of cardiac conditions based on electrocardiogram signals have become very significant for both patients and medical professionals. This article presents a novel method for classifying Electrocardiogram signals into arrhythmia (ARR), congestive heart failure (CHF), or normal sinus rhythm (NSR) using combined deep learning features and optimised Support Vector Machine (Op-SVM). First, to perform classification via Deep Learning (DL)the continuous wavelet transform (CWT) was used to transform one-dimensional (1-D) ECG signals into two-dimensional (2-D) images (scalograms) which are sent then to two pre-trained convolutional neural networks (CNN) architectures (ResNet50 and DenseNet201). Next, the features extracted from both CNNs were combined and fed to the SVM classifier. To enhance the performance of the classifier, Bayesian optimisation was used to optimise its hyperparameters. The suggested method was tested using a public dataset (PhysioNet) and evaluated using performance metric techniques. It succeeded in achieving values of 99.44 % for accuracy (Acc), 99.44 % for sensitivity (Sen), 99.72 % for specificity (Sp), and 99.44 % for precision (Pr), respectively, which are exceptional compared to the values produced by models considered to be state-of-the-art. Our results showed that the suggested method is suitable for in-clinic application in diagnosing cardiac conditions using ECG signals.