Multitask and Transfer Learning for Murmur Detection in Heart Sounds

João Costa1, Rui Rodrigues2, Paula Couto2
1University Institute of Lisbon - ISCTE and CAMGSD - IST/ULisboa, 2DM-FCT NOVA


We present a deep learning model for the automatic detection of murmurs and other cardiac abnormalities from the analysis of digital recordings of cardiac auscultations. This approach was developed in the context of the George B. Moody PhysioNet Challenge 2022.

More precisely, we consider multi-objective neural networks, with several Transformer blocks at their core, trained to perform 3 distinct tasks simultaneously: murmur detection, outcome classification and audio signal segmentation. We also perform pre-training with the 2016's Challenge data.

We entered the challenge under the team name matLisboa. Our (best) results on the hidden validation dataset (public Challenge leaderboard) were: