ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2020
Automatic detection of cortical arousals in sleep and their contribution to daytime sleepiness.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Automated deep learning-based arousal detection agrees with ten expert scorers in unseen data.Sleep · 2026Article
- Sleep architecture and rapid eye movement sleep without atonia in post-COVID-19 insomnia.Sleep · 2026Article
- Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Nature communications · 2026Article
- Automated sleep stage and event detection algorithms using quality-controlled polysomnography annotations.Sleep advances : a journal of the Sleep Research Society · 2026Article
- Automated analysis of the American Academy of Sleep Medicine Inter-Scorer Reliability gold-standard polysomnogram dataset.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Sleep stage mixing is associated with poor prognosis in early Parkinson's disease.NPJ Parkinson's disease · 2025Article
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- Article
- State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset.Scientific reports · 2024Article
- Artificial Intelligence Models for the Automation of Standard Diagnostics in Sleep Medicine-A Systematic Review.Bioengineering (Basel, Switzerland) · 2024Review
- Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction.Journal of thoracic disease · 2023Article
- Sleepiness in obstructive sleep apnea using hypopneas defined by a 3% oxygen desaturation or arousal but not by 4% or greater oxygen desaturation.Sleep & breathing = Schlaf & Atmung · 2022Article
- Age estimation from sleep studies using deep learning predicts life expectancy.NPJ digital medicine · 2022Article
- Genetic risk for subjective reports of insomnia associates only weakly with polygraphic measures of insomnia in 2,770 adults.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022Article
- Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times.PloS one · 2022Article
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7 authors.
Funding
Abstract
objectiveSignificant interscorer variability is found in manual scoring of arousals in polysomnographic recordings (PSGs). We propose a fully automatic method, the Multimodal Arousal Detector (MAD), for detecting arousals.
methodsA deep neural network was trained on 2,889 PSGs to detect cortical arousals and wakefulness in 1-second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects.
resultsIn a dataset of 1,026 PSGs, the MAD achieved an F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by nine expert technicians, the MAD performed comparable to four and significantly outperformed five expert technicians for arousal detection. After controlling for known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds (p = 0.0075).
conclusionsThe MAD performed better or comparable to human expert scorers. The MAD-predicted arousals were shown to be significant predictors of MSL. SIGNIFICANCE: This study validates a fully automatic method for scoring arousals in PSGs.
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