Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning - Fuadah, Yunendah Nur; Lim, Ki Moo (Frontiers in Physiology, 2022)
Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on electrocardiogram (ECG) signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each ECG signal condition. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, owing to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying a discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest, artificial neural network (ANN), and radial basis function network (RBFN), were investigated to provide the best system performance. This study obtained an accuracy of 100 % for the k-NN, SVM, random forest, and ANN classifiers, respectively, and 97% for RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.