HybDeepNet: A Hybrid Deep Learning Model for Detecting Cardiac Arrhythmia from ECG Signals

Abstract

The problem to be addressed is the high mortality rate of heart disease and the need for reliable and early detection techniques to prevent fatalities. Several clinical tests, including electrocardiogram (ECG) signals, heart sound signals, impedance cardiography (ICG), magnetic resonance imaging, and computer tomography can be used to determine whether an individual has heart disease. In this research, three deep learning models - Multilayer Perceptrons (MLPs), Deep Belief Networks (DBNs), and Restricted Boltzmann Machines (RBMs) - were used to detect heart disease by using the electrocardiogram (ECG) signal as the primary source. The publicly available datasets MIT-BIH and PTB-ECG were used to train and validate the proposed model. The results showed that the proposed hybrid model achieved the best performance compared to existing models, with an accuracy of 98.6%, 97.4%, and 96.2% on the MIT-BIH dataset, and 97.1%, 96.4%, and 95.3% on the PTB-ECG dataset, respectively. Furthermore, the model had excellent F1-score and AUC values, indicating the robustness of the proposed approach.