Classification of intracardiac heart sounds for ventricular and atrial fibrillation based on feature fusion and voting ensemble learning.

Arrhythmias, a significant category of cardiovascular diseases, including atrial fibrillation (AF) and ventricular fibrillation (VF), can result in severe complications and fatal outcomes, necessitating timely detection and intervention. This study proposes an intracardiac heart sound classification method based on feature fusion and voting ensemble learning for accurate discrimination between AF and VF. Using high-fidelity sonocardiogram dataset, the signals are preprocessed using a window function and a pre-emphasis filter. Then, the four types of features, including Mel-frequency cepstral coefficients, envelope autocorrelation, Hilbert-Huang transform, and wavelet scattering transform are extracted. Next, feature fusion and dimensionality reduction are performed using the maximum relevance minimum redundancy algorithm to streamline inputs for machine learning. Finally, a voting ensemble approach incorporating K-nearest neighbors, support vector machines, and artificial neural networks as base classifiers is employed to reliably distinguish between AF and VF. Experimental results demonstrate that the proposed method achieves an accuracy of 96.7%, with precision, recall, and F1 score of 96.5%, 96.8%, and 96.6%, respectively. By leveraging the complementary strengths of each base classifier, the proposed framework effectively classifies AF and VF from intracardiac heart sounds, thereby offering a reliable tool for abnormal intracardiac heart sound monitoring systems.