ArticleSleep medicine2015
The diagnostic value of power spectra analysis of the sleep electroencephalography in narcoleptic patients.
Article in Sleep medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Machine Learning for Diagnosis and Differentiation of Central Disorders of Hypersomnolence: A Systematic Review.European journal of neurology · 2026Pooled it
- Probability estimation of narcolepsy type 1 in DTA mice using unlabeled EEG and EMG data.Sleep advances : a journal of the Sleep Research Society · 2025Article
- A Review on Automated Sleep Study.Annals of biomedical engineering · 2024Review
- Excessive daytime sleepiness is associated with relative delta frequency power among patients with mild OSA.Frontiers in neurology · 2024Article
- Article
- Disrupted nighttime sleep and sleep instability in narcolepsy.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Review
- Increased EEG Theta Spectral Power in Sleep in Myotonic Dystrophy Type 1.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2018Article
- Role ofFrontiers in molecular neuroscience · 2017Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
Abstract
objectiveManifestations of narcolepsy with cataplexy (NC) include disturbed nocturnal sleep - hereunder sleep-wake instability, decreased latency to rapid eye movement (REM) sleep, and dissociated REM sleep events. In this study, we characterized the electroencephalography (EEG) of various sleep stages in NC versus controls.
methodsEEG power spectral density (PSD) was computed in 136 NC patients and 510 sex- and age-matched controls. Features reflecting differences in PSD curves were computed. A Lasso-regularized regression model was used to find an optimal feature subset, which was validated on 19 NC patients and 708 non-NC patients from a sleep clinic. Reproducible features were analyzed using receiver operating characteristic (ROC) curves.
resultsThirteen features were selected based on the training dataset. Three were applicable in the validation dataset, indicating that NC patients show (1) increased alpha power in REM sleep, (2) decreased sigma power in wakefulness, and (3) decreased delta power in stage N1 versus wakefulness. Sensitivity of these features ranged from 4% to 10% with specificity around 98%, and it did not vary substantially with and without treatment.
conclusionsEEG spectral analysis of REM sleep, wake, and differences between N1 and wakefulness contain diagnostic features of NC. These traits may represent sleepiness and dissociated REM sleep in patients with NC. However, the features are not sufficient for differentiating NC from controls, and further analysis is needed to completely evaluate the diagnostic potential of these features.
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