Sleep-wake transition in narcolepsy and healthy controls using a support vector machine.
Narcolepsy is characterized by abnormal sleep-wake regulation, causing sleep episodes during the day and nocturnal sleep disruptions. The transitions between sleep and wakefulness can be identified by manual scorings of a polysomnographic recording. The aim of this study was to develop an automatic...
| Publicado en: | Journal of Clinical Neurophysiology Vol. 31; no. 5; pp. 397 - 402 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
Oct2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109757817&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109757817 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07360258 8CL jtl: Journal of Clinical Neurophysiology issn: 07360258 maglogo: N pubinfo: dt: Oct2014 vid: 31 iid: 5 pid: 5086 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 109757817 109757817 2012757893 10.1097/WNP.0000000000000074 NLM25271675 109757817 ppf: 397 ppct: 5 formats: tig: atl: Sleep-wake transition in narcolepsy and healthy controls using a support vector machine. aug: au: Jensen, Julie B Sorensen, Helge B D Kempfner, Jacob Sørensen, Gertrud L Knudsen, Stine Jennum, Poul affil: *Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark; and tDanish Center for Sleep Medicine, Glostrup University Hospital, Glostrup, Denmark. sug: subj: Algorithms Brain Physiology Narcolepsy Physiopathology Sleep Physiology Wakefulness Physiology Adult Denmark Electroencephalography Electromyography Female Human Male Middle Age Pharmacokinetics Polysomnography Reproducibility of Results Young Adult Adult: 19-44 years Middle Aged: 45-64 years Female Male ab: Narcolepsy is characterized by abnormal sleep-wake regulation, causing sleep episodes during the day and nocturnal sleep disruptions. The transitions between sleep and wakefulness can be identified by manual scorings of a polysomnographic recording. The aim of this study was to develop an automatic classifier capable of separating sleep epochs from epochs of wakefulness by using EEG measurements from one channel. Features from frequency bands [alpha] (0-4 Hz), [beta] (4-8 Hz), [delta] (8-12 Hz), (12-16 Hz), 16 to 24 Hz, 24 to 32 Hz, 32 to 40 Hz, and 40 to 48 Hz were extracted from data by use of a wavelet packet transformation and were given as input to a support vector machine classifier. The classification algorithm was assessed by hold-out validation and 10-fold cross-validation. The data used to validate the classifier were derived from polysomnographic recordings of 47 narcoleptic patients (33 with cataplexy and 14 without cataplexy) and 15 healthy controls. Compared with manual scorings, an accuracy of 90% was achieved in the hold-out validation, and the area under the receiver operating characteristic curve was 95%. Sensitivity and specificity were 90% and 88%, respectively. The 10-fold cross-validation procedure yielded an accuracy of 88%, an area under the receiver operating characteristic curve of 92%, a sensitivity of 87%, and a specificity of 87%. Narcolepsy with cataplexy patients experienced significantly more sleep-wake transitions during night than did narcolepsy without cataplexy patients (P = 0.0199) and healthy subjects (P = 0.0265). In addition, the sleep-wake transitions were elevated in hypocretin-deficient patients. It is concluded that the classifier shows high validity for identifying the sleep-wake transition. Narcolepsy with cataplexy patients have more sleep-wake transitions during night, suggesting instability in the sleep-wake regulatory system. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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