A novel approach for SEMG signal classification with adaptive local binary pattern

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dc.authorid0000-0003-0710-0867en_US
dc.authorid0000-0001-5167-1101en_US
dc.contributor.authorErtuğrul, Ömer Faruk
dc.contributor.authorKaya, Yılmaz
dc.contributor.authorTekin, Ramazan
dc.date.accessioned2019-07-04T13:15:38Z
dc.date.available2019-07-04T13:15:38Z
dc.date.issued2015-12-31en_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.departmentBatman Üniversitesi Mühendislik - Mimarlık Fakültesi Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractFeature extraction plays a major role in the pattern recognition process, and this paper presents a novel feature extraction approach, adaptive local binary pattern (aLBP). aLBP is built on the local binary pattern (LBP), which is an image processing method, and one-dimensional local binary pattern (1D-LBP). In LBP, each pixel is compared with its neighbors. Similarly, in 1D-LBP, each data in the raw is judged against its neighbors. 1D-LBP extracts feature based on local changes in the signal. Therefore, it has high a potential to be employed in medical purposes. Since, each action or abnormality, which is recorded in SEMG signals, has its own pattern, and via the 1D-LBP these (hidden) patterns may be detected. But, the positions of the neighbors in 1D-LBP are constant depending on the position of the data in the raw. Also, both LBP and 1D-LBP are very sensitive to noise. Therefore, its capacity in detecting hidden patterns is limited. To overcome these drawbacks, aLBP was proposed. In aLBP, the positions of the neighbors and their values can be assigned adaptively via the down-sampling and the smoothing coefficients. Therefore, the potential to detect (hidden) patterns, which may express an illness or an action, is really increased. To validate the proposed feature extraction approach, two different datasets were employed. Achieved accuracies by the proposed approach were higher than obtained results by employed popular feature extraction approaches and the reported results in the literature. Obtained accuracy results were brought out that the proposed method can be employed to investigate SEMG signals. In summary, this work attempts to develop an adaptive feature extraction scheme that can be utilized for extracting features from local changes in different categories of time-varying signals.en_US
dc.identifier.citationErtuğrul, Ö F., Kaya, Y., Tekin, R. (2015). A novel approach for SEMG signal classification with adaptive local binary patterns. Medical & Biological Engineering & Computing, 54(7), pp. 1137-1146. https://doi.org/10.1007/s11517-015-1443-zen_US
dc.identifier.endpage1146en_US
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.issue7en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1137en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-015-1443-z
dc.identifier.urihttps://hdl.handle.net/20.500.12402/2193
dc.identifier.volume54en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.isversionof10.1007/s11517-015-1443-zen_US
dc.relation.journalMedical and Biological Engineering and Computingen_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.subjectAdaptive Signal Processingen_US
dc.subjectBiomedical Signal Processingen_US
dc.subjectExtracting Local Featuresen_US
dc.subjectFeature Extractionen_US
dc.subjectLocal Binary Patternsen_US
dc.subjectTime Signalsen_US
dc.titleA novel approach for SEMG signal classification with adaptive local binary patternen_US
dc.typeArticleen_US

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