A novel framework of two successive feature selection levels using weight-based procedure for voice-loss detection in parkinson's disease

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dc.authorid0000-0003-3217-6185en_US
dc.authorid0000-0003-1814-9682en_US
dc.authorid0000-0003-1840-9958
dc.contributor.authorAshour, Amira S.
dc.contributor.authorNour, Majid Kamal A.
dc.contributor.authorPolat, Kemal
dc.contributor.authorGuo, Yanhui
dc.contributor.authorAlsaggaf, Wafaa
dc.contributor.authorEl-Attar, Amira
dc.date.accessioned2021-06-23T19:54:52Z
dc.date.available2021-06-23T19:54:52Z
dc.date.issued2020
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractParkinson & x2019;s disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/impact of the top-selected PCA- and ECFS- features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94 & x0025; accuracy for voice-loss detection in PD, while, with the same classifier, 88 & x0025; accuracy was achieved without using the proposed selection method.en_US
dc.identifier.doi10.1109/ACCESS.2020.2989032
dc.identifier.endpage76203en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85084428908en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage76193en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.2989032
dc.identifier.urihttps://hdl.handle.net/20.500.12491/10668
dc.identifier.volume8en_US
dc.identifier.wosWOS:000530801500014en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParkinson's Diseaseen_US
dc.subjectVoice Lossen_US
dc.subjectFeature Extractionen_US
dc.subjectFeature Selectionen_US
dc.subjectPrincipal Component Analysisen_US
dc.subjectEigenvector Centrality Feature Selectionen_US
dc.titleA novel framework of two successive feature selection levels using weight-based procedure for voice-loss detection in parkinson's diseaseen_US
dc.typeArticleen_US

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