Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds

TL;DR

This paper introduces a self-supervised learning method using CNNs for automatic detection of heart murmurs from audio recordings, achieving competitive accuracy in a challenge setting.

Contribution

It demonstrates the effectiveness of self-supervised learning with data augmentation for heart murmur detection from phonocardiogram sounds.

Findings

Achieved a weighted accuracy of 0.737 in murmur detection

Ranked 13th out of 40 teams in the challenge

Used multiple augmentation strategies to improve representation learning

Abstract

Heart murmurs are abnormal sounds present in heartbeats, caused by turbulent blood flow through the heart. The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome. The recordings are captured from multiple locations around the heart. Our participation investigates the effectiveness of selfsupervised learning for murmur detection. We train the layers of a backbone CNN in a self-supervised way with data from both this year's and the 2016 challenge. We use two different augmentations on each training sample, and normalized temperature-scaled cross-entropy loss. We experiment with different augmentations to learn effective phonocardiogram representations. To build the final detectors we train two classification heads, one for each challenge task. We present evaluation results for all combinations of the available augmentations, and for our multipleaugmentation approach. Our team's, Listen2YourHeart, SSL murmur detection classifier received a weighted accuracy score of 0.737 (ranked 13th out of 40 teams) and an outcome identification challenge cost score of 11946 (ranked 7th out of 39 teams) on the hidden test set.