Overcomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusion

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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.authorwosidKahya, Yasemin P/Q-1766-2015
dc.authorwosidSerbes, Gorkem/AAZ-8822-2020
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorSerbes, Gorkem
dc.contributor.authorKahya, Yasemin P.
dc.date.accessioned2024-06-12T11:13:08Z
dc.date.available2024-06-12T11:13:08Z
dc.date.issued2017
dc.departmentTrakya Üniversitesien_US
dc.description.abstractBackground and objective: Crackle, wheeze and normal lung sound discrimination is vital in diagnosing pulmonary diseases. Previous works suffer from limited frequency resolution and lack of the ability to deal with oscillatory signals (wheezes). The main objective of this study is to propose a novel wavelet based lung sound classification system that is capable of adaptively representing crackle, wheeze and normal lung sound signal time-frequency properties. Methods: A method which is based on rational dilation wavelet transform is proposed to classify lung sounds into three main categories, namely, normal, wheeze and crackle. Six different feature extraction methods were used with five different classifiers all of which were compared with the proposed method on 600, lung sound episodes in a cross validation scheme. Six statistical subset features were extracted from raw features and fed into classifiers. After comparative evaluation of the proposed method, an ensemble learning scheme was built to increase the performance of the proposed method. Results: It has been shown that performance of the proposed method was superior to previous methods in terms of accuracy. Moreover, its computational time was far less than its nearest competitor (S transform). It has also been shown that the proposed method was able to cope with oscillatory type signals as well as transient sounds performing 95.17% average accuracy for energy subset and 97.38% ensemble average accuracy showing a promising time-frequency tool for biological signals. Conclusions: The proposed method has shown better performance even using only one subset of extracted features. It provides better time-frequency resolution for all types of signals of interest and is less redundant than continuous wavelet transform and significantly faster than its nearest competitor. (C) 2017 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipBogazigi University Research Fund [16A02D2]; Turkish Scientific Technical Research Council (TUBITAK) [2211]en_US
dc.description.sponsorshipThis work was supported by Bogazigi University Research Fund under grant number 16A02D2. The work of S. Ulukaya is supported by the Ph.D. scholarship (2211) from Turkish Scientific Technical Research Council (TUBITAK). The authors express their gratitude to Sibel Yurt, MD for her guidance on data acquisition procedure. The authors would like to thank Dr. Ipek Sen from the Department of Electrical and Electronics Engineering at Beykent University for her contribution in data acquisition and interpretation phase.en_US
dc.identifier.doi10.1016/j.bspc.2017.06.018
dc.identifier.endpage336en_US
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85025163933en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage322en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2017.06.018
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23440
dc.identifier.volume38en_US
dc.identifier.wosWOS:000409290400032en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPulmonary Sounden_US
dc.subjectRational Dilation Wavelet Transformen_US
dc.subjectQ-Factoren_US
dc.subjectEnsemble Learningen_US
dc.subjectAdventitious Lung Soundsen_US
dc.subjectStatistical Feature Extractionen_US
dc.subjectNeural-Networken_US
dc.subjectLung Soundsen_US
dc.subjectClassificationen_US
dc.subjectFrequencyen_US
dc.titleOvercomplete discrete wavelet transform based respiratory sound discrimination with feature and decision level fusionen_US
dc.typeArticleen_US

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