Classification of Pathological and Healthy Voice Using Perceptual Wavelet Packet Decomposition and Support Vector Machine

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dc.authorscopusid57203165669
dc.contributor.authorArslan, Özkan
dc.date.accessioned2022-05-11T14:03:04Z
dc.date.available2022-05-11T14:03:04Z
dc.date.issued2020
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Elektronik ve Haberleşme Mühendisliği Bölümü
dc.description2020 Medical Technologies Congress, TIPTEKNO 2020 -- 19 November 2020 through 20 November 2020 -- -- 166140
dc.description.abstractIn this study, a new approach has been presented based on perceptual wavelet packet transform and support vector machines for analysis and classification of pathological and healthy voice signals. Feature extraction and development of classification algorithm play important role in the area of automatic classification of pathological and healthy voice signals. The critical sub-bands are obtained by separating pathological and healthy voice signals with perceptual wavelet packet trans- form. The energy and entropy measures are extracted at each sub-bands used for training and testing of the classifier. In the study, the VIOCED database are used and it consists of 208 voice signals which are 58 healthy and 150 pathological. Experimental results demonstrate that the proposed features and classification algorithm provide 93.1% sensitivity, 96.5% specificity and 97.1% accuracy rates and it shows that the proposed method can be used to help medical professionals for diagnosing pathological status of a voice signal. © 2020 IEEE.
dc.identifier.doi10.1109/TIPTEKNO50054.2020.9299290
dc.identifier.isbn978-1728180731
dc.identifier.scopus2-s2.0-85099484075
dc.identifier.urihttps://doi.org/10.1109/TIPTEKNO50054.2020.9299290
dc.identifier.urihttps://hdl.handle.net/20.500.11776/4595
dc.identifier.wosWOS:000659419900073
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorArslan, Özkan
dc.language.isotr
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofTIPTEKNO 2020 - Tip Teknolojileri Kongresi - 2020 Medical Technologies Congress, TIPTEKNO 2020
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectenergy
dc.subjectentropy
dc.subjectperceptual wavelet packet
dc.subjectBiomedical engineering
dc.subjectClassification (of information)
dc.subjectDiagnosis
dc.subjectSupport vector machines
dc.subjectWavelet analysis
dc.subjectWavelet decomposition
dc.subjectAutomatic classification
dc.subjectClassification algorithm
dc.subjectEnergy and entropies
dc.subjectMedical professionals
dc.subjectNew approaches
dc.subjectTraining and testing
dc.subjectWavelet Packet Decomposition
dc.subjectWavelet packet transforms
dc.subjectBiomedical signal processing
dc.titleClassification of Pathological and Healthy Voice Using Perceptual Wavelet Packet Decomposition and Support Vector Machine
dc.title.alternativeAlgisal Dalgacik Paket Dönüsümü ve Destek Vektör Makineleri Kullanarak Patolojik ve Saglikli Seslerin Siniflandirilmasi]
dc.typeConference Object

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