Explainable Deep Learning for Non-Invasive Detection of Pulmonary Artery Hypertension from Heart Sounds

Abstract

Late diagnoses of patients affected by pulmonary artery hypertension (PH) have a poor outcome. This observation has led to a call for earlier, non-invasive PH detection. Cardiac auscultation offers a non-invasive and cost-effective alternative to both right heart catheterization and doppler analysis in analysis of PH. We propose to detect PH via analysis of digital heart sound recordings with over-parameterized deep neural networks. In contrast with previous approaches in the literature, we assess the impact of a pre-processing step aiming to separate S2 sound into the aortic (A2) and pulmonary (P2) components. We obtain an area under the ROC curve of. 95, improving over our adaptation of a state-of-the-art Gaussian mixture model PH detector by +.17. Post-hoc explanations and analysis show that the availability of separated A2 and P2 components contributes significantly to prediction. Analysis of stethoscope heart sound recordings with deep networks is an effective, low-cost and non-invasive solution for the detection of pulmonary hypertension.