H.6.5.13. Signal processing
Samira Moghani; Hossein Marvi; Zeynab Mohammadpoory
Abstract
This study introduces a novel classification framework based on Deep Orthogonal Non-Negative Matrix Factorization (Deep ONMF), which leverages scalogram representations of phonocardiogram (PCG) signals to hierarchically extract structural features crucial for detecting valvular heart diseases (VHDs). ...
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This study introduces a novel classification framework based on Deep Orthogonal Non-Negative Matrix Factorization (Deep ONMF), which leverages scalogram representations of phonocardiogram (PCG) signals to hierarchically extract structural features crucial for detecting valvular heart diseases (VHDs). Scalograms, generated via the Continuous Wavelet Transform (CWT), serve as the foundational input to the proposed feature extraction pipeline, which integrates them with Deep ONMF in a unified and segmentation-free architecture. The resulting scalogram–Deep ONMF framework is designed to hierarchically extract features through two complementary perspectives: Scale-Domain Analysis (SDA) and Temporal-Domain Analysis (TDA). These extracted features are then classified using shallow classifiers, with Random Forest (RF) achieving the best results, particularly when paired with SDA features based on the Bump wavelet. Experimental evaluations on two public PCG datasets—one with five heart sound classes and another with binary classification—demonstrate the effectiveness of the proposed method, achieving high classification accuracies of up to 98.40% and 97.23%, respectively, thereby confirming its competitiveness with state-of-the-art techniques. The results suggest that the proposed approach offers a practical and powerful solution for automated heart sound analysis, with potential applications beyond VHD detection.
