Observational studyJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine2024
Validation of a sleep staging classification model for healthy adults based on two combinations of a single-channel EEG headband and wrist actigraphy.
Observational study in Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04943562 (Evaluation of the Viability of Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging in Comparison With Polysomnography), which is not on this map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Evaluation of the Viability of Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging in Comparison With Polysomnography
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- A gradual and sustainable approach to diagnosing sleep and circadian disturbances in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- 2024 Standardization of Polysomnography Reports - A Consensus of the Brazilian Sleep Association.Sleep science (Sao Paulo, Brazil) · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesThe aim of this study was to develop a sleep staging classification model capable of accurately performing on different wearable devices.
methodsTwenty-three healthy participants underwent a full-night type I polysomnography and used two device combinations: (A) flexible single-channel electroencephalogram (EEG) headband + actigraphy (n = 12) and (B) rigid single-channel EEG headband + actigraphy (n = 11). The signals were segmented into 30-second epochs according to polysomnographic stages (scored by a board-certified sleep technologist; model ground truth) and 18 frequency and time features were extracted. The model consisted of an ensemble of bagged decision trees. Bagging refers to bootstrap aggregation to reduce overfitting and improve generalization. To evaluate the model, a training dataset under 5-fold cross-validation and an 80-20% dataset split was used. The headbands were also evaluated without the actigraphy feature. Participants also completed a usability evaluation (comfort, pain while sleeping, and sleep disturbance).
resultsCombination A had an F1-score of 98.4% and the flexible headband alone of 97.7% (error rate for N1: combination A = 9.8%; flexible headband alone = 15.7%). Combination B had an F1-score of 96.9% and the rigid headband alone of 95.3% (error rate for N1: combination B = 17.0%; rigid headband alone = 27.7%); in both, N1 was more confounded with N2.
conclusionsWe developed an accurate sleep classification model based on a single-channel EEG device, and actigraphy was not an important feature of the model. Both headbands were found to be useful, with the rigid one being more disruptive to sleep. Future research can improve our results by applying the developed model in a population with sleep disorders. CLINICAL
trial registrationRegistry: ClinicalTrials.gov; Name: Actigraphy, Wearable EEG Band and Smartphone for Sleep Staging; URL: https://clinicaltrials.gov/study/NCT04943562; Identifier: NCT04943562. CITATION: Melo MC, Vallim JRS, Garbuio S, et al. Validation of a sleep staging classification model for healthy adults based on 2 combinations of a single-channel EEG headband and wrist actigraphy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.