Resonance based separation and energy based classification of lung sounds using tunable wavelet transform

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dc.authoridUlukaya, Sezer/0000-0003-0473-7547;
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.authorwosidUlukaya, Sezer/HJY-5331-2023
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.issued2021
dc.departmentTrakya Üniversitesien_US
dc.description.abstractBackground and objective: The locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/ wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. Methods: In the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Qfactor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. Results: Results have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. Conclusions: It is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach. Background and objective: The locations and occurrence pattern of adventitious sounds in the respiratory cycle have critical diagnostic information. In a lung sound sample, the crackles and wheezes may exist individually or they may coexist in a successive/overlapping manner superimposed onto the breath noise. The performance of the linear time-frequency representation based signal decomposition methods has been limited in the crackle/ wheeze separation problem due to the common signal components that may arise in both time and frequency domain. However, the proposed resonance based decomposition can be used to isolate crackles and wheezes which behave oppositely in time domain even if they share common frequency bands. Methods: In the proposed study, crackle and/or wheeze containing synthetic and recorded lung-sound signals were decomposed by using the resonance information which is produced by joint application of the Tunable Q factor Wavelet Transform and Morphological Component Analysis. The crackle localization and signal reconstruction performance of the proposed approach was compared with the previously suggested Independent Component Analysis and Empirical Mode Decomposition methods in a quantitative and qualitative manner. Additionally, the decomposition ability of the proposed approach was also used to discriminate crackle and wheeze waveforms in an unsupervised way by employing signal energy. Results: Results have shown that the proposed approach has significant superiority over its competitors in terms of the crackle localization and signal reconstruction ability. Moreover, the calculated energy values have revealed that the transient crackles and rhythmic wheezes can be successfully decomposed into low and high resonance channels by preserving the discriminative information. Conclusions: It is concluded that previous works suffer from deforming the waveform of the crackles whose time domain parameters are vital in computerized diagnostic classification systems. Therefore, a method should provide automatic and simultaneous decomposition ability, with smaller root mean square error and higher accuracy as demonstrated by the proposed approach.en_US
dc.description.sponsorshipBogazici University Research Fund [16A02D2, 2211]; Turkish Scientific and Technological Research Council (TUBITAK)en_US
dc.description.sponsorshipThis work was supported by Bogazici University Research Fund under grant number 16A02D2. S. Ulukaya was supported by the Ph.D. scholarship (2211) from Turkish Scientific and Technological Research Council (TUBITAK).en_US
dc.identifier.doi10.1016/j.compbiomed.2021.104288
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.pmid33676336en_US
dc.identifier.scopus2-s2.0-85101837296en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2021.104288
dc.identifier.urihttps://hdl.handle.net/20.500.14551/23606
dc.identifier.volume131en_US
dc.identifier.wosWOS:000631622900001en_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 Sounden_US
dc.subjectTunable Q Factor Wavelet Transformen_US
dc.subjectCrackleen_US
dc.subjectWheezeen_US
dc.subjectMorphological Component Analysisen_US
dc.subjectIndependent Component Analysisen_US
dc.subjectVesicular Soundsen_US
dc.subjectRespiratory Soundsen_US
dc.subjectMode Decompositionen_US
dc.subjectCracklesen_US
dc.subjectExtractionen_US
dc.subjectAlgorithmen_US
dc.titleResonance based separation and energy based classification of lung sounds using tunable wavelet transformen_US
dc.typeArticleen_US

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