Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

Martin Gjoreski,Gregor Poglajen,Borut Budna,Anton Gradisek,Matjaz Gams

doi:10.1109/access.2020.2968900

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.

Highlights

Chronic heart failure (CHF) is a chronic, progressive condition underscored by the heart’s inability to supply enough perfusion to target tissues and organs at the physiological filling pressures to meet their metabolic demands [1]
We present the analysis of the method’s performance with respect to the segment size. (ii) The proposed end-to-end Deep Learning (DL) architecture learns both from the temporal representation of the signal and the spectral representation of the signal, whereas most of the approaches in the related works use end-to-end learning in one of the domains only. (iii) We used the PhysioNet Challenge datasets to evaluate our approach and to provide a comparison with the challenge baseline method; we used our own dataset, which, in addition to including the typical healthy vs. patient labels, is labeled for the specific CHF phase, i.e., compensated and decompensated for some of the patients
In this paper, we presented a novel method for CHF detection from PCG audio recordings

Summary

Introduction

Chronic heart failure (CHF) is a chronic, progressive condition underscored by the heart’s inability to supply enough perfusion to target tissues and organs at the physiological filling pressures to meet their metabolic demands [1]. In the typical clinical course of CHF, we observe alternating episodes of compensated phases, when the patient feels well and does not display symptoms and signs of fluid overload, and decompensated phases, when symptoms and signs of systemic fluid overload (such as breathlessness, orthopnea, peripheral edema, liver congestion, pulmonary edema) can be observed.

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