ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2021
Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Evaluating Electroencephalogram-Based Predictive Model for Drowsiness Measurement to Reduce Accident Risk in Active Individuals: Protocol for a Preliminary Monocentric Study.JMIR research protocols · 2026Article
- Automated diagnostic method for sleep apnea and hypopnea using overnight airflow and oxygen saturation.MethodsX · 2025Article
- Classification of internet addiction using machine learning on electroencephalography synchronization and functional connectivity.Psychological medicine · 2025Article
- Efficient Neural Network Classification of Parkinson's Disease and Schizophrenia Using Resting-State EEG Data.Brain topography · 2025Article
- Evaluating reliability in wearable devices for sleep staging.NPJ digital medicine · 2024Review
- Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.Sleep & breathing = Schlaf & Atmung · 2023Review
- Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.Frontiers in artificial intelligence · 2023Article
- Spotlight on Sleep Stage Classification Based on EEG.Nature and science of sleep · 2023Article
- Brain optimization with additional study time: potential brain differences between high- and low-performance college students.Frontiers in psychology · 2023Article
- Advancing to the next epoch in how we teach scoring.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
- Frontal grey matter microstructure is associated with sleep slow waves characteristics in late midlife.Sleep · 2022Article
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- Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network.Nature and science of sleep · 2021Article
- Sleep, circadian rhythms and outcomes in intensive care unit.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesTo assess the performance of the single-channel automatic sleep staging (AS) software ASEEGA in adult patients diagnosed with various sleep disorders.
methodsSleep recordings were included of 95 patients (38 women, 40.5 ± 13.7 years) diagnosed with insomnia (n = 23), idiopathic hypersomnia (n = 24), narcolepsy (n = 24), and obstructive sleep apnea (n = 24). Visual staging (VS) was performed by two experts (VS1 and VS2) according to the American Academy of Sleep Medicine rules. AS was based on the analysis of a single electroencephalogram channel (Cz-Pz), without any information from electro-oculography nor electromyography. The epoch-by-epoch agreement (concordance and Conger's coefficient [κ]) was compared pairwise (VS1-VS2, AS-VS1, AS-VS2) and between AS and consensual VS. Sleep parameters were also compared.
resultsThe pairwise agreements were: between AS and VS1, 78.6% (κ = 0.70); AS and VS2, 75.0% (0.65); and VS1 and VS2, 79.5% (0.72). Agreement between AS and consensual VS was 85.6% (0.80), with the following distribution: insomnia 85.5% (0.80), narcolepsy 83.8% (0.78), idiopathic hypersomnia 86.1% (0.68), and obstructive sleep disorder 87.2% (0.82). A significant low-amplitude scorer effect was observed for most sleep parameters, not always driven by the same scorer. Hypnograms obtained with AS and VS exhibited very close sleep organization, except for 80% of rapid eye movement sleep onset in the group diagnosed with narcolepsy missed by AS.
conclusionsAgreement between AS and VS in sleep disorders is comparable to that reported in healthy individuals and to interexpert agreement in patients. ASEEGA could therefore be considered as a complementary sleep stage scoring tool in clinical practice, after improvement of rapid eye movement sleep onset detection.
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