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.