Fundamental Heart Sound Classification using the Continuous Wavelet Transform and Convolutional Neural Networks.

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dc.contributor.author Meintjes, Andries en
dc.contributor.author Lowe, Andrew en
dc.contributor.author Legget, Malcolm en
dc.date.accessioned 2019-06-13T02:02:51Z en
dc.date.issued 2018-07 en
dc.identifier.citation IEEE Engineering in Medicine and Biology Society. Annual Conference 2018:409-412 Jul 2018 en
dc.identifier.issn 1557-170X en
dc.identifier.uri http://hdl.handle.net/2292/46980 en
dc.description.abstract Correct identification of the fundamental heart sounds is an important step in identifying the heart cycle stages. Heart valve pathologies can cause abnormal heart sounds or extra sounds, and an important distinguishing feature between different pathologies is the timing of these extra sounds in the heart cycle. In the design of an understandable heart sound analysis system, heart sound segmentation is an indispensable step. In this study classification of the fundamental heart sounds using continuous wavelet transform (CWT) scalograms and convolutional neural networks (CNN) is investigated. Classification between the first and second heart sound of scalograms produced by the Morse analytic wavelet was compared for CNN, support vector machine (SVM), and knearest neighbours (kNN) classifiers. Samples of the first and second heart sound were extracted from a publicly available dataset of normal and abnormal heart sound recordings, and magnitude scalograms were calculated for each sample. These scalograms were used to train and test CNNs. Classification using features extracted from a fully connected layer of the network was compared with linear binary pattern features. The CNN achieved an average classification accuracy of 86% when distinguishing between the first and second heart sound. Features extracted from the CNN and classified using a SVM achieved similar results (85.9%). Classification of the CNN features outperformed LBP features using both SVM and kNN classifiers. The results indicate that there is significant potential for the use of CWT and CNN in the analysis of heart sounds. en
dc.format.medium Print en
dc.relation.ispartofseries Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://www.ieee.org/publications/rights/author-posting-policy.html en
dc.subject Humans en
dc.subject Heart Sounds en
dc.subject Neural Networks (Computer) en
dc.subject Wavelet Analysis en
dc.subject Support Vector Machine en
dc.title Fundamental Heart Sound Classification using the Continuous Wavelet Transform and Convolutional Neural Networks. en
dc.type Conference Item en
dc.identifier.doi 10.1109/embc.2018.8512284 en
pubs.begin-page 409 en
pubs.volume 2018 en
dc.rights.holder Copyright: IEEE en
pubs.end-page 412 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.elements-id 757489 en
pubs.org-id Medical and Health Sciences en
pubs.org-id School of Medicine en
pubs.org-id Medicine Department en
pubs.record-created-at-source-date 2018-11-17 en
pubs.dimensions-id 30440420 en


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