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Aim: The George B. Moody PhysioNet Challenge 2022 raised problems of heartmurmur detection and related abnormal cardiac function identification fromphonocardiograms (PCGs). This work describes the novel approaches developed byour team, Revenger, to solve these problems.  Methods: PCGs were resampled to 1000 Hz, then filtered with a Butterworthband-pass filter of order 3, cutoff frequencies 25 - 400 Hz, and z-scorenormalized. We used the multi-task learning (MTL) method via hard parametersharing to train one neural network (NN) model for all the Challenge tasks. Weperformed neural architecture searching among a set of network backbones,including multi-branch convolutional neural networks (CNNs), SE-ResNets,TResNets, simplified wav2vec2, etc.  Based on a stratified splitting of the subjects, 20% of the public data wasleft out as a validation set for model selection. The AdamW optimizer wasadopted, along with the OneCycle scheduler, to optimize the model weights.  Results: Our murmur detection classifier received a weighted accuracy scoreof 0.736 (ranked 14th out of 40 teams) and a Challenge cost score of 12944(ranked 19th out of 39 teams) on the hidden validation set.  Conclusion: We provided a practical solution to the problems of detectingheart murmurs and providing clinical diagnosis suggestions from PCGs.

Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram