The Utility of fNIRS Signals versus Self-Report for Classification of Fibromyalgia Syndrome

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dc.contributor.authorEken, Aykut
dc.contributor.authorGökçay, Didem
dc.contributor.authorBaskak, Bora
dc.contributor.authorBaltacı, Ayşegül
dc.contributor.authorKara, Murat
dc.date.accessioned2020-04-30T23:34:36Z
dc.date.available2020-04-30T23:34:36Z
dc.date.issued2017
dc.departmentDÜ, Mühendislik Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.description25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYen_US
dc.descriptionEken, Aykut/0000-0002-7023-7930en_US
dc.descriptionWOS: 000413813100133en_US
dc.description.abstractFibromyalgia (FM) is a widespread painful disease that has a 2-8% prevalence. Its diagnosis is generally performed by American College of Rheumatology (ACR) criteria. However, these criteria are subjective and their reliability is controversial. In this study, painful stimulation and Transcutaneous Electrical Nerve Stimulation (TENS) were applied to both hands of healthy controls and FM patients and hemodynamic responses was measured by using Functional Near Infrared Spectroscopy (fNIRS). Features extracted from hemodynamic responses and self-report data were used with 4 different classifiers and 14 different parameters. In conclusion, classification performed by objective data collected from fNIRS signals (95%) gave higher accuracy than classification performed by subjective self-report data (83%). This study showed that painful stimulation and TENS application can be used to diagnose Fibromyalgia disease by using fNIRS.en_US
dc.description.sponsorshipTurk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Univen_US
dc.description.sponsorshipHigher Education Council (Yuksekogretim Kurumu - YOK)en_US
dc.description.sponsorshipWe would like to thank to Cemre Topcu for her help during data collection, This study was supported by Higher Education Council (Yuksekogretim Kurumu - YOK).en_US
dc.identifier.isbn978-1-5090-6494-6
dc.identifier.issn2165-0608
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5193
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2017 25Th Signal Processing And Communications Applications Conference (Siu)en_US
dc.relation.ispartofseriesSignal Processing and Communications Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectfNIRSen_US
dc.subjectTENSen_US
dc.subjectFibromyalgiaen_US
dc.subjectClassificationen_US
dc.subjectSelf-Reporten_US
dc.titleThe Utility of fNIRS Signals versus Self-Report for Classification of Fibromyalgia Syndromeen_US
dc.typeConference Objecten_US

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