Hilbert-envelope features for cardiac disease classification from noisy phonocardiograms

Abstract

Cardiovascular disease (CVD) is considered a significant public health concern around the world. Automated early diagnostic tools for CVDs can provide substantial benefits, especially in low-resource countries. This study proposes a time-domain Hilbert envelope feature (HEF) extraction scheme that can effectively distinguish among different cardiac anomalies from heart sounds even in highly noisy recordings. The method is motivated by how a cardiologist listens to the heart murmur configurations, e.g., the intensity of the heart sound envelope over a cardiac cycle. The proposed feature is invariant to the heart rate, the position of the first and second heart sounds, and robust in extracting the murmur configuration pattern in the presence of respiratory noise. Experimental evaluations are performed compared to two different state-of-the-art methods in the presence of respiratory noise with signal-to-noise ratio (SNR) values ranging from 0–15 dB. The proposed HEF, fused with standard acoustic and Resnet features, yields an average accuracy, sensitivity, specificity, and F1-score of 94.78% (±2.63), 87.48 %(±6.07), 96.87% (±1.51) and 87.47% (±5.94), respectively, while using a random forest (RF) classifier and applied on a mixture of clean and noise mixed recordings of an open-access dataset. Compared to the best-performing baseline model, this feature-fusion scheme provides a significant performance improvement (p<0.05), notably achieving an absolute improvement of 6.16% in averaged sensitivity. When applied to noisy heart sound recordings collected from a local hospital, this method significantly outperforms the existing systems by achieving an average accuracy of 80.54% (±2.65) and sensitivity of 68.52% (±4.54). The achieved sensitivity yields an absolute improvement of 12.65% compared to the best-performing baseline model in this real-world dataset.