Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique

Basit Öğe Kaydı
dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authoridSerbes, Gorkem/0000-0003-4591-7368
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
dc.authorwosidSerbes, Gorkem/AAZ-8822-2020
dc.authorwosidKahya, Yasemin P/Q-1766-2015
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorSerbes, Gorkem
dc.contributor.authorKahya, Yasemin P.
dc.date.accessioned2024-06-12T11:13:37Z
dc.date.available2024-06-12T11:13:37Z
dc.date.issued2019
dc.departmentTrakya Üniversitesien_US
dc.description.abstractBackground and objective: Wheezes in pulmonary sounds are anomalies which are often associated with obstructive type of lung diseases. The previous works on wheeze-type classification focused mainly on using fixed time-frequency/scale resolution based on Fourier and wavelet transforms. The main contribution of the proposed method, in which the time-scale resolution can be tuned according to the signal of interest, is to discriminate monophonic and polyphonic wheezes with higher accuracy than previously suggested time and time-frequency/scale based methods. Methods: An optimal Rational Dilation Wavelet Transform (RADWT) based peak energy ratio (PER) parameter selection method is proposed to discriminate wheeze types. Previously suggested Quartile Frequency Ratios, Mean Crossing Irregularity, Multiple Signal Classification, Mel-frequency Cepstrum and Dyadic Discrete Wavelet Transform approaches are also applied and the superiority of the proposed method is demonstrated in leave-one-out (LOO) and leave-one-subject-out (LOSO) cross validation schemes with support vector machine (SVM), k nearest neighbor (k-NN) and extreme learning machine (ELM) classifiers. Results: The results show that the proposed RADWT based method outperforms the state-of-the-art time, frequency, time-frequency and time-scale domain approaches for all classifiers in both LOO and LOSO cross validation settings. The highest accuracy values are obtained as 86% and 82.9% in LOO and LOSO respectively when the proposed PER features are fed into SVM. Conclusions: It is concluded that time and frequency domain characteristics of wheezes are not steady and hence, tunable time-scale representations are more successful in discriminating polyphonic and monophonic wheezes when compared with conventional fixed resolution representations.en_US
dc.description.sponsorshipBogazici University Research Fund, Turkey [16A02D2, 2211]; Turkish Scientific and Technological Research Council (TUBITAK), Turkeyen_US
dc.description.sponsorshipThis work was supported by Bogazici University Research Fund, Turkey under grant number 16A02D2. S. Ulukaya was supported by the Ph.D. scholarship (2211) from Turkish Scientific and Technological Research Council (TUBITAK), Turkey.en_US
dc.identifier.doi10.1016/j.compbiomed.2018.11.004
dc.identifier.endpage182en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid30496939en_US
dc.identifier.scopus2-s2.0-85057128776en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage175en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2018.11.004
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23607
dc.identifier.volume104en_US
dc.identifier.wosWOS:000456751100018en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers In Biology And Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRespiratory Soundsen_US
dc.subjectPulmonary Soundsen_US
dc.subjectDiscriminationen_US
dc.subjectWheezingen_US
dc.subjectMonophonicen_US
dc.subjectPolyphonicen_US
dc.subjectLung Sound Analysisen_US
dc.subjectRespiratory Soundsen_US
dc.titleWheeze type classification using non-dyadic wavelet transform based optimal energy ratio techniqueen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu