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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Original languageEnglish
Title of host publicationComputing in Cardiology (CinC) 2022
Place of PublicationTampere, Finland
Published10 Sep 2022

Documents

  • 439_Preprint

    439_Preprint.pdf, 0.99 MB, application/pdf

    Uploaded date:01 Nov 2022

    Version:Accepted author manuscript

King's Authors

Abstract

Computer-aided analysis is of great help in improving heart sound classification. PhysioNet Challenge 2022 provides a platform for researchers to test and compare 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 (HMSNet) 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 1st and 4th out of 305 entries, respectively.

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