Determination of freezing of gait with wearable sensors in patients with Parkinson's disease

Basit Öğe Kaydı
dc.contributor.authorEliiyi, Uğur
dc.contributor.authorKeskinoğlu, Pembe
dc.contributor.authorKahraman, Turhan
dc.contributor.authorÖzkurt, Ahmet
dc.contributor.authorYürdem, Betül
dc.contributor.authorDuran, G.
dc.contributor.authorGenç, Aslı
dc.date.accessioned2022-02-15T16:57:38Z
dc.date.available2022-02-15T16:57:38Z
dc.date.issued2020
dc.departmentBakırçay Üniversitesien_US
dc.descriptionMovement-Disorder-Society (MDS) International Virtual Congress -- SEP 12-16, 2020 -- ELECTR NETWORKen_US
dc.description.abstractObjective: The aim was to determine freezing of gait (FOG) with wearable sensors in patients with Parkinson’s disease (PD). Background: PD is a neurodegenerative disorder leading to deficits in automatic motor performance. FOG is a major mobility problem for patients with PD, can be accompanied by postural instability and subsequent falls. Accurate and automatic FOG detection are essential for long-term symptom monitoring or preventing FOG via cueing. Although some studies have investigated the use of wearable sensors to detect FOG, conducted mostly with participants who were mainly in early stages of PD, there is no firm consensus regarding appropriate methodologies. Method: This study had a diagnostic accuracy design. Multi-segmental acceleration data was obtained from 12 patients with PD performing standardized tasks, and clinical assessment of FOG was performed by an experienced neurologist in real time and from video recordings. Three-axis wireless accelerometers were attached to patients’ ankles, waist and wrists. Trials were performed during the drug-free period, at least 12 hours after taking medication. The standardized tasks included standing from a chair, walking, 180o and 360o turnings, and passing a doorway. Trials were repeated at least 3 times, and up to 5 times if there was no FOG event. Sensor signals were processed as numerical data. Results: The mean age of patients was 64 years, 58% of them were males, and mean of disease duration was 10 years. Modified Hoehn and Yahr scale scores ranged from 2.5 to 4. The data matrix dimension is 103,261*18. Random forest (RF), artificial neural network (ANN), and decision tree (DT) methods, which are among the supervised learning algorithms, were used to predict FOG in the presence of misleading tremors on this big data. Algorithms’ performances were AUCRF/ANN/DT= 0.985 / 0.962 / 0.765 and sensitivityRF/ANN/DT= 97.1% / 94.3% / 93.8%. Conclusion: The predictions of ANN and RF were much better, while the sensitivity of DT was close to other methods. FOG detection is important to prevent it before occurring and decrease its effects. In this study, it has been shown that FOG can be detected by using the proposed algorithms with data collected from wearable sensors in patients with PD, even who are in late stages of PD.en_US
dc.description.sponsorshipMovement Disorder Socen_US
dc.identifier.endpageS648en_US
dc.identifier.issn0885-3185
dc.identifier.issn1531-8257
dc.identifier.startpageS648en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14034/231
dc.identifier.volume35en_US
dc.identifier.wosWOS:000567989803102en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.journalMovement Disordersen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleDetermination of freezing of gait with wearable sensors in patients with Parkinson's diseaseen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu
İşletme Bölümü Koleksiyonu