EMG Signal Classification by Extreme Learning Machine

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTekin, Ramazan/0000-0003-4325-6922
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidKAYA, Yılmaz/C-3822-2017
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTekin, Ramazan/I-1519-2014
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTagluk, M. Emin
dc.contributor.authorKaya, Yilmaz
dc.contributor.authorTekin, Ramazan
dc.date.accessioned2024-08-04T20:37:44Z
dc.date.available2024-08-04T20:37:44Z
dc.date.issued2013
dc.departmentİnönü Üniversitesien_US
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUSen_US
dc.description.abstractFrom disease detection to action assessment EMG signals are used variety of field. Miscellaneous studies have been conducted toward analysis of EMG signals. In this study some statistical features of signal were derived, the best evocative features were selected via Linear Discriminant Analysis (LDA) and feature vectors were constructed. This analytic feature vectors were classified through Extreme Learning Machine (ELM). 8 channel EMG signals recorded from 10 normal and 10 aggressive actions were used as an example. By cross-comparison of the obtained results to the ones obtained via various feature identifying methods (AR coefficients, wavelet energy and entropy) and classification methods (NB, SVM, LR, ANN, PART, Jrip, J48 and LMT) the success of the proposed method was determined.en_US
dc.identifier.isbn978-1-4673-5563-6
dc.identifier.isbn978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-84880897525en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/96156
dc.identifier.wosWOS:000325005300110en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2013 21st Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectEMGen_US
dc.subjectDiscriminant Analysisen_US
dc.subjectExtreme Learning Machineen_US
dc.subjectstatistical parametersen_US
dc.titleEMG Signal Classification by Extreme Learning Machineen_US
dc.typeConference Objecten_US

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