Hierarchical Multi-Scale Convolutional Network for Murmurs Detection on PCG Signals

Computer-aided analysis is helpful in improving heart sound classification. PhysioNet Challenge 2022 provides a platform for researchers to evaluate their proposed classification algorithms. In the Challenge, our team (HearTech+) proposed a recording quality assessment method based on frequency density distribution for label correction to prevent the poor-quality recording segments from misleading network optimisation. Besides, a hierarchical multi-scale convolutional neural network (HMS-Net) was designed to conduct both the murmur
$(T1)$
and clinical outcome
$(T2)$
classification. HMS-Net extracts convolutional features from the spectrograms on multiple scales and fuses them through its hierarchical architecture. The network builds long short-term independencies between multi-scale features and improves the classification performance. Finally, the prediction of a patient is based on the ensembled segment predictions by sliding window. In the five-fold cross-validation by patients, the proposed algorithm performed an average weighted accuracy of 0.81 (best 0.853) on
$T1$
and an average challenge score of 9808 (best 9242) on
$T2$
. In the Challenge hidden validation set, the proposed algorithm achieved 0.806 weighted accuracy on
$T1$
and 9120 challenge score on
$T2$
, ranking 1
^st
and
$4^{th}$
out of 305 entries, respectively. In the final hidden testing set,
$T1$
was 0.776 ranking
$2^{nd}$
, and
$T2$
was 12069.