Machine Learning-based ECG Classification using Wavelet Scattered Features

Abstract

Cardiac abnormalities are one of the leading causes of mortality and morbidity among the population. Changes in the morphology and rhythm of the cardiac signals associated with cardiac abnormalities need to be identified and classified. Advances in artificial intelligence pave the way for precise classification. The preprocessed ECG signal segments undergo wavelet scattering to extract the low variance features with reduced dimensions are rearranged and the key features are selected using Minimum Redundancy and Maximum Relevance (MRMR) feature selection algorithms chosen by comparatively analyzing different feature selection algorithms and the selected features are fed to the machine learning models. Classification of ECG signals is comparatively analyzed using different Machine Learning models such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision tree, and Artificial Neural Network (ANN) models with 10-fold cross-validation. The performance is improved by optimizing each model by tuning the hyperparameters. Among the twenty models, the cubic SVM model achieves the highest accuracy of 99.84 percent.