Key result
Machine learning detects heart failure decompensation with ~72% accuracy from phonocardiograms, outperforming cardiologists.
Why the study?
Early detection of chronic heart failure decompensation is essential but limited by data scarcity and lack of accurate PCG-based diagnostic methods.
Does automated analysis of phonocardiograms using machine learning and PCGmix data augmentation improve the detection of heart failure decompensation?
Does automated analysis of phonocardiograms using machine learning and PCGmix data augmentation improve the detection of heart failure decompensation?
Absolute Event Rate: 72% vs 50%
Machine learning analysis of phonocardiograms using a novel data augmentation technique (PCGmix) improves the automated detection of chronic heart failure decompensation in data-scarce environments.
Cardiovascular diseases (CVDs), including chronic heart failure (CHF), represent a major global health challenge. Early detection is essential but often limited by data scarcity. This paper explores two key contributions to address this issue: detection of CHF decompensation using phonocardiogram (PCG) recordings and machine learning, and PCGmix, a novel data-augmentation technique tailored to heart sounds. In a study with 37 CHF patients, our models classified decompensated vs. recompensated states with up to 72\% accuracy. PCGmix further improves diagnostic performance in scenarios with limited training data, achieving comparable accuracy to models trained on datasets up to 50\% larger without augmentation.
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David Susič (2026) studied Chronic heart failure (n=37). Machine learning analysis of phonocardiogram recordings vs. Cardiologists' assessment based solely on sound was evaluated on Classification accuracy of decompensated vs. recompensated states. Machine learning models using phonocardiogram recordings classified decompensated versus recompensated states with 72% accuracy, compared to 50% average accuracy achieved by cardiologists.
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