DETECTION OF CHRONIC HEART FAILURE USING ML & DL

Abstract

Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is
increasing by 2% annually. Despite the significant burden that CHF poses and despite the
ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce,
even in the research community. We present a method for CHF detection based on heart sounds.
The method combines classic Machine-Learning (ML) and end-to-end Deep Learning (DL). The
classic ML learns from expert features, and the DL learns from a spectro-temporal representation
of the signal. The method was evaluated on recordings from 947 subjects from six publicly
available datasets and one CHF dataset that was collected for this study. Using the same
evaluation method as a recent PhysoNet challenge, the proposed method achieved a score of
89.3, which is 9.1 higher than the challenge's baseline method. The method's aggregated
accuracy is 92.9% (error of 7.1%); while the experimental results are not directly comparable,
this error rate is relatively close to the percentage of recordings labeled as “unknown” by experts
(9.7%). Finally, we identified 15 expert features that are useful for building ML models to
differentiate between CHF phases (i.e., in the decompensated phase during hospitalization and in
the recompensated phase) with an accuracy of 93.2%. The proposed method shows promising
results both for the distinction of recordings between healthy subjects and patients and for the
detection of different CHF phases. This may lead to the easier identification of new CHF patients
and the development of home-based CHF monitors for avoiding hospitalizations.