Feature Extraction Using Time-Frequency Analysis for Monophonic-Polyphonic Wheeze Discrimination

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547;
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidKahya, Yasemin P/Q-1766-2015
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorSen, Ipek
dc.contributor.authorKahya, Yasemin P.
dc.date.accessioned2024-06-12T11:23:48Z
dc.date.available2024-06-12T11:23:48Z
dc.date.issued2015
dc.departmentTrakya Üniversitesien_US
dc.description37th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC) -- AUG 25-29, 2015 -- Milan, ITALYen_US
dc.description.abstractThe aim of this study is monophonic-polyphonic wheeze episode discrimination rather than the conventional wheeze (versus non-wheeze) episode detection. We used two different methods for feature extraction to discriminate monophonic and polyphonic wheeze episodes. One of the methods is based on frequency analysis and the other is based on time analysis. Frequency analysis based method uses ratios of quartile frequencies to exploit the difference in the power spectrum. Time analysis based method uses mean crossing irregularity to exploit the difference in periodicity in the time domain. Both methods are applied on the data before and after an image processing based preprocessing step. Calculated features are used in classification both individually and in combinations. Support vector machine, k-nearest neighbor and Naive Bayesian classifiers are adopted in leave-one-out scheme. A total of 121 monophonic and 110 polyphonic wheeze episodes are used in the experiments, where the best classification performances are 71.45% for time domain based features, 68.43% for frequency domain based features, and 75.78% for a combination of selected best features.en_US
dc.description.sponsorshipIEEE Engn Med & Biol Socen_US
dc.identifier.endpage5415en_US
dc.identifier.isbn978-1-4244-9270-1
dc.identifier.issn1557-170X
dc.identifier.issn1558-4615
dc.identifier.pmid26737515en_US
dc.identifier.scopus2-s2.0-84953237735en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage5412en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14551/26654
dc.identifier.wosWOS:000371717205168en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2015 37th Annual International Conference Of The Ieee Engineering In Medicine And Biology Society (Embc)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleFeature Extraction Using Time-Frequency Analysis for Monophonic-Polyphonic Wheeze Discriminationen_US
dc.typeConference Objecten_US

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