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Abnormal heart sounds detected from short duration unsegmented phonocardiograms by wavelet entropy

Langley, Philip; Murray, Alan

Authors

Philip Langley

Alan Murray



Abstract

© 2016 CCAL. Segmentation of the characteristic heart sounds is thought to be an essential requirement for the automatic classification of phonocardiograms. The aim of this work was to test the feasibility of classification using short duration, unsegmented recordings. Recordings from the 2016 PhysioNet/Computing in Cardiology Challenge were analysed. Wavelet entropy of unsegmented 5 s duration recordings was calculated and the optimum wavelet scale and wavelet entropy threshold determined from the training set. The algorithm was validated on the test set. At a wavelet scale of 1.7 wavelet entropy was significantly reduced in abnormal recordings (median (IQR), 6.3 (1.8) vs 8.0 (1.8) p

Citation

Langley, P., & Murray, A. Abnormal heart sounds detected from short duration unsegmented phonocardiograms by wavelet entropy

Presentation Conference Type Conference Paper (published)
Acceptance Date Apr 1, 2016
Publication Date Mar 1, 2016
Deposit Date Apr 21, 2022
Publicly Available Date Aug 1, 2022
Journal Computing in Cardiology
Print ISSN 2325-8861
Peer Reviewed Not Peer Reviewed
Volume 43
Pages 545-548
ISBN 9781509008964
DOI https://doi.org/10.22489/cinc.2016.156-268
Public URL https://hull-repository.worktribe.com/output/3590849

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