Detection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyography

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dc.authoridumut, ilhan/0000-0002-5269-1128
dc.authoridCentik, Guven/0000-0001-9034-1586
dc.authorwosidumut, ilhan/A-2772-2017
dc.contributor.authorUmut, Ilhan
dc.contributor.authorCentik, Guven
dc.date.accessioned2024-06-12T11:20:59Z
dc.date.available2024-06-12T11:20:59Z
dc.date.issued2016
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe number of channels used for polysomnographic recording frequently causes difficulties for patients because of the many cables connected. Also, it increases the risk of having troubles during recording process and increases the storage volume. In this study, it is intended to detect periodic leg movement (PLM) in sleep with the use of the channels except leg electromyography (EMG) by analysing polysomnography (PSG) data with digital signal processing (DSP) and machine learning methods. PSG records of 153 patients of different ages and genders with PLM disorder diagnosis were examined retrospectively. A novel software was developed for the analysis of PSG records. The software utilizes the machine learning algorithms, statistical methods, and DSP methods. In order to classify PLM, popular machine learning methods (multilayer perceptron, K-nearest neighbour, and random forests) and logistic regression were used. Comparison of classified results showed that while K-nearest neighbour classification algorithm had higher average classification rate (91.87%) and lower average classification error value (RMSE = 0.2850), multilayer perceptron algorithm had the lowest average classification rate (83.29%) and the highest average classification error value (RMSE = 0.3705). Results showed that PLM can be classified with high accuracy (91.87%) without leg EMG record being present.en_US
dc.identifier.doi10.1155/2016/2041467
dc.identifier.issn1748-670X
dc.identifier.issn1748-6718
dc.identifier.pmid27213008en_US
dc.identifier.scopus2-s2.0-84973115831en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1155/2016/2041467
dc.identifier.urihttps://hdl.handle.net/20.500.14551/25877
dc.identifier.volume2016en_US
dc.identifier.wosWOS:000375677700001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherHindawi Ltden_US
dc.relation.ispartofComputational And Mathematical Methods In Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject[No Keywords]en_US
dc.titleDetection of Periodic Leg Movements by Machine Learning Methods Using Polysomnographic Parameters Other Than Leg Electromyographyen_US
dc.typeArticleen_US

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