Identification of Heart Sounds for the Analysis of Cardiac Pathology Using Machine Learning
Abstract
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.References
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Susič, D.(2024). Identification of heart sounds for the analysis of cardiac pathology using machine learning [Doctoral dissertation, Jožef Stefan Interational Postgraduate School
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https://doi.org/10.31449/inf.v50i2.8753Downloads
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