Listen to your heart: Α self-supervised approach for detecting murmur in heart-beat sounds

Aristotelis Ballas1, Vasileios Papapanagiotou2, Anastasios Delopoulos2, Christos Diou3
1Harokopio University of Athens, Department of Informatics and Telematics, 2Aristotle University of Thessaloniki, 3Harokopio University of Athens


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 self-supervised learning for murmur detection. We evaluate the use of a backbone CNN, whose layers are trained 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 multiple-augmentation approach.