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dc.contributor.authorLi, Fan
dc.contributor.authorTang, Hong
dc.contributor.authorShang, Shang
dc.contributor.authorMathiak, Klaus
dc.contributor.authorCong, Fengyu
dc.date.accessioned2020-06-12T06:39:02Z
dc.date.available2020-06-12T06:39:02Z
dc.date.issued2020
dc.identifier.citationLi, F., Tang, H., Shang, S., Mathiak, K., & Cong, F. (2020). Classification of Heart Sounds Using Convolutional Neural Network. <i>Applied Sciences</i>, <i>10</i>(11), Article 3956. <a href="https://doi.org/10.3390/app10113956" target="_blank">https://doi.org/10.3390/app10113956</a>
dc.identifier.otherCONVID_35913918
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/69902
dc.description.abstractHeart sounds play an important role in the diagnosis of cardiac conditions. Due to the low signal-to-noise ratio (SNR), it is problematic and time-consuming for experts to discriminate different kinds of heart sounds. Thus, objective classification of heart sounds is essential. In this study, we combined a conventional feature engineering method with deep learning algorithms to automatically classify normal and abnormal heart sounds. First, 497 features were extracted from eight domains. Then, we fed these features into the designed convolutional neural network (CNN), in which the fully connected layers that are usually used before the classification layer were replaced with a global average pooling layer to obtain global information about the feature maps and avoid overfitting. Considering the class imbalance, the class weights were set in the loss function during the training process to improve the classification algorithm’s performance. Stratified five-fold cross-validation was used to evaluate the performance of the proposed method. The mean accuracy, sensitivity, specificity and Matthews correlation coefficient observed on the PhysioNet/CinC Challenge 2016 dataset were 86.8%, 87%, 86.6% and 72.1% respectively. The proposed algorithm’s performance achieves an appropriate trade-off between sensitivity and specificity.en
dc.format.mimetypeapplication/pdf
dc.languageeng
dc.language.isoeng
dc.publisherMDPI
dc.relation.ispartofseriesApplied Sciences
dc.rightsCC BY 4.0
dc.subject.otherautomatic heart sound classification
dc.subject.otherfeature engineering
dc.subject.otherconvolutional neural network
dc.titleClassification of Heart Sounds Using Convolutional Neural Network
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202006124147
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2076-3417
dc.relation.numberinseries11
dc.relation.volume10
dc.type.versionpublishedVersion
dc.rights.copyright© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights.accesslevelopenAccessfi
dc.subject.ysoneuroverkot
dc.subject.ysodiagnostiikka
dc.subject.ysokoneoppiminen
dc.subject.ysotiedonlouhinta
dc.subject.ysosydäntaudit
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p7292
jyx.subject.urihttp://www.yso.fi/onto/yso/p416
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p5520
jyx.subject.urihttp://www.yso.fi/onto/yso/p2710
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.3390/app10113956
jyx.fundinginformationThis research was funded by the National Natural Science Foundation of China, grant number 91748105 & 81471742; the Fundamental Research Funds for the Central Universities [DUT2019] in Dalian University of Technology in China.
dc.type.okmA1


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