Toward Early Detection and Monitoring of Chronic Heart Failure Using Heart Sounds

• Autores: Martin Gjoreski, Anton Gradišek, Borut Budna, Matjaž Gams, Gregor Poglajen
• Localización: Intelligent environments 2019: Workshop Proceedings of the 15th International Conference on Intelligent Environments / coord. por Andrés Muñoz Ortega, Sofia Ouhbi, Wolfgang Minker, Loubna Echabbi, Miguel Navarro Cía, 2019, ISBN 9781614999836, págs. 336-343
• Idioma: inglés
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• Resumen

  • Chronic heart failure (CHF) affects over 26 million of people worldwide and represents a significant societal, logistic and financial burden both for the patients and for the healthcare system, necessitating novel management approaches of this patient population. In this paper, we explore the possibilities of detecting heart failure worsening based on heart sounds using machine-learning methods. First, we developed a method that distinguishes between healthy individuals and those with a decompensated CHF episode. Our method includes filtering, segmentation, feature extraction, and machine learning, and was tested with a leave- one-subject-out evaluation technique on the data from 193 individuals. The method achieved 82% accuracy, outperforming the baseline classifier for 14 percentage points. In the next stage, we explored the differences between decompensated and recompensated states of CHF patients. We identified ten features for which there is statistically significant difference (p<0.001) in the features distributions, when calculated between decomensated and recompensated state of CHF. These features may be the key for developing algorithms for continuous personalized remote monitoring of the CHF patients.