ArticleJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2019
Automatic Sleep Staging in Patients With Obstructive Sleep Apnea Using Single-Channel Frontal EEG.
Article in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Performance Investigation of Somfit Sleep Staging Algorithm.Nature and science of sleep · 2024Trial
- Validation of manually scored multichannel frontal electroencephalography against polysomnography in a paediatric cohort.Journal of sleep research · 2025Article
- A flexible and dissolving traditional Chinese medicine microneedle patch for sleep-aid intervention.Heliyon · 2024Article
- An Autonomous Sleep-Stage Detection Technique in Disruptive Technology Environment.Sensors (Basel, Switzerland) · 2024Article
- Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.Scientific reports · 2023Article
- Validation of photoplethysmography- and acceleration-based sleep staging in a community sample: comparison with polysomnography and Actiwatch.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2023Article
- An Attention-Guided Spatiotemporal Graph Convolutional Network for Sleep Stage Classification.Life (Basel, Switzerland) · 2022Article
- Evaluation of a Single-Channel EEG-Based Sleep Staging Algorithm.International journal of environmental research and public health · 2022Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesReliable sleep staging is difficult to obtain from home sleep testing for diagnosis of obstructive sleep apnea (OSA), especially when it is self-applied. Hence, the current study aimed to develop a single frontal electroencephalography-based automatic sleep staging system (ASSS).
methodsThe ASSS system was developed on a clinical dataset, with a high percentage of participants with OSA. The F4-M1 signal extracted from 62 participants (62.9% having OSA) was used to build a four-stage classifier. Performance of the ASSS was tested in a holdout set of 58 patients (60.3% having OSA) with epoch-by-epoch and whole-night agreement for sleep staging compared with expert scoring of polysomnography.
resultsMean all-stage percentage agreement was 75.52% (95% confidence interval, 72.90 to 78.13) (kappa 0.62; 95% confidence interval, 0.58 to 0.65), with mean percentage agreement for wake, light sleep, deep sleep (DS), and rapid eye movement of 78.04%, 70.97%, 83.65%, and 75.00%, respectively. The whole-night agreement was good-excellent (intraclass correlation coefficient, 0.74 to 0.88) for sleep onset latency, wake after sleep onset, total sleep time, and sleep efficiency. Compared to the non-OSA subset, the OSA subset had lower agreement for DS.
conclusionsOur results indicate that a single-channel F4-M1 based ASSS was sufficient for sleep staging in a population with a high percentage of participants with OSA.
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