A combined machine learning and deep learning method using heart sounds detected chronic heart failure with an aggregated accuracy of 92.9%.
Does a combined machine learning and deep learning algorithm analyzing heart sounds accurately detect chronic heart failure and its clinical phases?
A combined machine learning and deep learning approach using heart sounds can accurately detect chronic heart failure and differentiate between compensated and decompensated phases, offering a potential tool for non-invasive home monitoring.
Absolute Event Rate: 89.3% vs 80.2%
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.
Gjoreski et al. (Wed,) conducted a other in Chronic heart failure (n=947). Combined classic Machine-Learning and end-to-end Deep Learning vs. PhysioNet challenge baseline method was evaluated on PhysioNet challenge score for CHF detection. A combined machine learning and deep learning method using heart sounds detected chronic heart failure with an aggregated accuracy of 92.9%.