Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
Benjamin Walker Felix Krones Ivan Kiskin Guy Parsons Terry Lyons Adam Mahdi
This study presents our team PathToMyHeart's contribution to the George B.
Moody PhysioNet Challenge 2022. Two models are implemented. The first model is
a Dual Bayesian ResNet (DBRes), where each patient's recording is segmented
into overlapping log mel spectrograms. These undergo two binary
classifications: present versus unknown or absent, and unknown versus present
or absent. The classifications are aggregated to give a patient's final
classification. The second model is the output of DBRes integrated with
demographic data and signal features using XGBoost.DBRes achieved our best
weighted accuracy of $0.771$ on the hidden test set for murmur classification,
which placed us fourth for the murmur task. (On the clinical outcome task,
which we neglected, we scored 17th with costs of $12637$.) On our held-out
subset of the training set, integrating the demographic data and signal
features improved DBRes's accuracy from $0.762$ to $0.820$. However, this
decreased DBRes's weighted accuracy from $0.780$ to $0.749$. Our results
demonstrate that log mel spectrograms are an effective representation of heart
sound recordings, Bayesian networks provide strong supervised classification
performance, and treating the ternary classification as two binary
classifications increases performance on the weighted accuracy.