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 results on the hidden test dataset were: Murmur score (weighted accuracy): 0.735 (ranked 15th). Outcomes score (cost): 12593 (ranked 16th).
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Multitask and Transfer Learning for Cardiac Abnormality Detections in Heart Sounds
Semantic Scholar · Medicine · 2022
Abstract
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 results on the hidden test dataset were: Murmur score (weighted accuracy): 0.735 (ranked 15th). Outcomes score (cost): 12593 (ranked 16th).