Detection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gait

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dc.authorid0000-0003-0710-0867en_US
dc.authorid0000-0001-5167-1101en_US
dc.authorid0000-0003-4325-6922en_US
dc.authorid0000-0003-2763-4452en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorKaya, Yılmaz
dc.contributor.authorTekin, Ramazan
dc.contributor.authorAlmalı, Mehmet Nuri
dc.date.accessioned2019-07-04T13:14:17Z
dc.date.available2019-07-04T13:14:17Z
dc.date.issued2016-09en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe Parkinson's disease (PD) is one of the most common diseases, especially in elderly people. Although the previous studies showed that the PD can be diagnosed by expert systems through its cardinal symptoms such as the tremor, muscular rigidity, disorders of movements and voice, it was reported that the presented approaches, which utilize simple motor tasks, were limited and lack of standardization. To achieve a standard approach in PD detection, an approach, which is built on shifted one-dimensional local binary patterns (Shifted 1D-LBP) and machine learning methods, was proposed. Shifted 1D-LBP is built on 1D-LBP, which is sensitive to local changes in a signal. In 1D-LBP the positions of neighbors around center data are constant and therefore, the number of patterns that can be exacted by it is limited. This drawback was solved by Shifted 1D-LBP by changeable positions of neighbors. In evaluation and validation stages, the Gait in Parkinson's Disease (gaitpdb) dataset, which consists of three gait datasets that were recorded in different tasks or experiment protocols, were employed. Statistical features were exacted from formed histograms of gait signals transformed by Shifted 1D-LBP. Whole features and selected features were classified by machine learning methods. Obtained results were compared with statistical features exacted from signals in both time and frequency domains and results reported in the literature. Achieved results showed that the proposed approach can be successfully employed in PD detection from gait. This work is not only an attempt to develop a PD detection method, but also a general-purpose approach that is based on detecting local changes in time ordered signals.en_US
dc.identifier.citationErtuğrul, Ö F., Kaya, Y., Tekin, R., Almalı, M. N. (2016). Detection of Parkinsons disease by Shifted One Dimensional Local Binary Patterns from gait. Expert Systems with Applications, 56, pp. 156-163. https://doi.org/10.1016/j.eswa.2016.03.018en_US
dc.identifier.endpage163en_US
dc.identifier.issn0957-4174
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage156en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2016.03.018
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2191
dc.identifier.volume56en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.isversionof10.1016/j.eswa.2016.03.018en_US
dc.relation.journalExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.rightsAttribution-NonCommercial-ShareAlike 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.subjectAutomatic Diagnosisen_US
dc.subjectBiomedicalen_US
dc.subjectExpert Systemsen_US
dc.subjectGaiten_US
dc.subjectParkinson's Diseaseen_US
dc.subjectShifted One-Dimensional Local Binary Patternen_US
dc.titleDetection of Parkinson's disease by Shifted One Dimensional Local Binary Patterns from gaiten_US
dc.typeArticleen_US

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