ArticleSleep medicine2018
Evaluation of an automated pipeline for large-scale EEG spectral analysis: the National Sleep Research Resource.
Article in Sleep medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Slow Wave Activity During Nocturnal Sleep in Hypersomnolence Disorder.Journal of sleep research · 2026Article
- Comparing Manual and Automatic Artifact Detection in Sleep EEG Recordings.Psychophysiology · 2025Article
- High-frequency neural activity dysregulation is associated with sleep and psychiatric disorders in BMAL1-deficient animal models.iScience · 2024Article
- Effects of gender and age on sleep EEG functional connectivity differences in subjects with mild difficulty falling asleep.Frontiers in psychiatry · 2024Article
- Metadata recommendations for light logging and dosimetry datasets.BMC digital health · 2024Article
- Relationship between the Spectral Power Density of Sleep Electroencephalography and Psychiatric Symptoms in Patients with Breathing-related Sleep Disorder.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2021Article
- Spectral sleep electroencephalographic correlates of sleep efficiency, and discrepancies between actigraphy and self-reported measures, in older men.Journal of sleep research · 2021Article
- Difference in spectral power density of sleep EEG between patients with simple snoring and those with obstructive sleep apnoea.Scientific reports · 2020Article
- High-Resolution Spectral Sleep Analysis Reveals a Novel Association Between Slow Oscillations and Memory Retention in Elderly Adults.Frontiers in aging neuroscience · 2020Article
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11 authors.
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Abstract
STUDY
objectivesWe present an automated sleep electroencephalogram (EEG) spectral analysis pipeline that includes an automated artifact detection step, and we test the hypothesis that spectral power density estimates computed with this pipeline are comparable to those computed with a commercial method preceded by visual artifact detection by a sleep expert (standard approach).
methodsEEG data were analyzed from the C3-A2 lead in a sample of polysomnograms from 161 older women participants in a community-based cohort study. We calculated the sensitivity, specificity, accuracy, and Cohen's kappa measures from epoch-by-epoch comparisons of automated to visual-based artifact detection results; then we computed the average EEG spectral power densities in six commonly used EEG frequency bands and compared results from the two methods using correlation analysis and Bland-Altman plots.
resultsAssessment of automated artifact detection showed high specificity [96.8%-99.4% in non-rapid eye movement (NREM), 96.9%-99.1% in rapid eye movement (REM) sleep] but low sensitivity (26.7%-38.1% in NREM, 9.1-27.4% in REM sleep). However, large artifacts (total power > 99th percentile) were removed with sensitivity up to 87.7% in NREM and 90.9% in REM, with specificities of 96.9% and 96.6%, respectively. Mean power densities computed with the two approaches for all EEG frequency bands showed very high correlation (≥0.99). The automated pipeline allowed for a 100-fold reduction in analysis time with regard to the standard approach.
conclusionDespite low sensitivity for artifact rejection, the automated pipeline generated results comparable to those obtained with a standard method that included manual artifact detection. Automated pipelines can enable practical analyses of recordings from thousands of individuals, allowing for use in genetics and epidemiological research requiring large samples.
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