Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Detection and classification of cardiovascular diseases are crucial for early diagnosis and prediction of heart-related conditions. Existing methods rely on either electrocardiogram or phonocardiogram signals, resulting in higher false positive rates. Solely ECG misses the murmurs associated with the narrowing of the blood vessels caused by abnormalities in the heart. Similarly, considering only PCG will miss the subtle changes in the electrical activity of the heart that leads to incomplete evaluation. The implementation of a multi-class heart disease classification model utilizing both ECG and PCG signals is the objective of the proposed study. The approach involves preprocessing, fusion, waveform detection utilizing the Pan-Tompkins Algorithm, and signal localization using Algebraic Integer-quantized Stationary Wavelet Transform. Low-rank Kernelized Density-Based Spatial Clustering of Applications with noise is employed to cluster signals into normal and abnormal categories. Feature selection is performed with Heming Wayed Polar Bear Optimization, and classification is done using C squared Pool Sign BI-power-activated Deep Convolutional Neural Network. The proposed model achieves a classification accuracy of 97% with 0.03 error rate. The multi-class classifier effectively identifies and classifies the heart diseases into Aortic stenosis Valvular disorder, Tricuspid Valvular disorder, Mitralstenosis Valvular disorder, Pulmonary Valvular disorder, Atrial Fibrillation, and Ischemic heart disorder.