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 classifier capable of separating sleep epochs from epochs of wakefulness by using EEG measurements from one channel. Features from frequency bands α (0-4 Hz), β (4-8 Hz), δ (8-12 Hz), θ (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.