Phonocardiogram Classification Using 1-Dimensional Inception Time Convolutional Neural Networks

Abstract

Murmurs are sounds caused by turbulent blood flow that are often the first sign of structural heart disease. These sounds are detected by auscultating the heart using a stethoscope, or more recently by a phonocardiogram (PCG). We aim to identify the presence, absence, or un-clear cases of murmurs, as well as predict normal or abnormal clinical outcome from PCG recordings using machine learning. We trained and tested two 1-dimensional convolutional neural networks (CNN) on a PCG data set from a pediatric population of 1568 individuals. One model predicted mur-murs, while the other model predicted clinical outcomes. Both models were trained to give recording-wise predictions, while the final predictions were given for every patient (patient wise predictions). This paper describes our participation in the George B. Moody PhysioNet Challenge 2022 whose objective was to identify heart murmurs and clinical outcome from PCGs. Our team, Simulab, trained a clinical outcome classifier that achieved a challenge cost score of 12419 (ranked 14th out of 39 teams) and the murmur classifier achieved a weighted accuracy of 0.593 (ranked 30th out of 40 teams) on the test set.