Detection of Freezing of Gait Episodes in Patients with Parkinson's Disease using Electroencephalography and Motion Sensors: A Protocol and its Feasibility Results

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dc.authoridEliiyi, Ugur/0000-0002-5584-891X
dc.authoridKahraman, Turhan/0000-0002-8776-0664
dc.authoridOzkurt, Ahmet/0000-0001-6404-7244
dc.authorwosidEliiyi, Ugur/Q-1810-2019
dc.authorwosidKahraman, Turhan/O-5499-2015
dc.authorwosidOzkurt, Ahmet/S-9970-2018
dc.contributor.authorEliiyi, Ugur
dc.contributor.authorKahraman, Turhan
dc.contributor.authorGenc, Arzu
dc.contributor.authorKeskinoglu, Pembe
dc.contributor.authorOzkurt, Ahmet
dc.contributor.authorDonmez, BerrilColakoglu
dc.date.accessioned2024-03-09T18:48:48Z
dc.date.available2024-03-09T18:48:48Z
dc.date.issued2022
dc.departmentİzmir Bakırçay Üniversitesien_US
dc.description.abstractObjective: Freezing of gait (FOG) is an important concern for both patients with Parkinson's disease (pwPD) and physicians. In this study, we aimed to introduce a study protocol and our initial data. The data were subsequently used in machine learning models to detect FOG episodes using brain activity signals and motion data in the laboratory setting using complex FOG-evoking activities in a sample of pwPD with and without FOG compared with age-matched healthy controls. Subjects and Methods: An experimental task to evoke a FOG episode was designed. This experimental task was tested on two pwPD with FOG in on and off periods and one healthy control. Brain activity signals and motion data were collected simultaneously using electroencephalography (EEG) and inertial measurement units (IMUs). Results: The whole procedure took about 2 h, during which around 30 min were spent on walking tasks, involving 35 complete tours in the designed 8-m hallway by pwPD. Both EEG and IMUs sensor data could be collected, accompanied by FOG episode data marked by the neurologist. The video recordings of the patient's walking tasks were checked and reanalyzed by the neurologist sometime after the data experiment for marking the beginnings and ends of the observed FOG episodes more precisely. In the end, 24 stops were marked as FOG, which corresponded to 11% of the sensor data collected during the walking tasks. Conclusion: The designed FOG-evoking task protocol could be performed without any adverse effects, and it created enough FOG episodes for analysis. EEG and motion sensor data could be successfully collected without any significant artifacts.en_US
dc.description.sponsorshipDepartment of Scientific Research Projects, Dokuz Eylul University (Izmir, Turkiye) [2018.KB.SAG.005]en_US
dc.description.sponsorshipThis work was funded by the Department of Scientific Research Projects, Dokuz Eylul University (Izmir, Turkiye), under grant number 2018.KB.SAG.005.en_US
dc.identifier.doi10.4103/nsn.nsn_104_22
dc.identifier.endpage205en_US
dc.identifier.issn2636-865X
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-85145489720en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage200en_US
dc.identifier.trdizinid1167650en_US
dc.identifier.urihttps://doi.org/10.4103/nsn.nsn_104_22
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1167650
dc.identifier.urihttps://hdl.handle.net/20.500.14034/1485
dc.identifier.volume39en_US
dc.identifier.wosWOS:000970788600006en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.publisherWolters Kluwer Medknow Publicationsen_US
dc.relation.ispartofNeurological Sciences and Neurophysiologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectElectroencephalography; Freezing; Gait; Motion; Parkinson's Diseaseen_US
dc.titleDetection of Freezing of Gait Episodes in Patients with Parkinson's Disease using Electroencephalography and Motion Sensors: A Protocol and its Feasibility Resultsen_US
dc.typeArticleen_US

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