Articlenpj biomedical innovations2025
Evaluating the performance of wearable EEG sleep monitoring devices: a meta-analysis approach.
Article in npj biomedical innovations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Wearable EEG sleep monitoring devices (wEEGs) are increasingly popular in both clinical and consumer applications. However, their performance compared to polysomnography (PSG), the gold standard, remains under study. This meta-analysis of 43 validation studies assessed wEEGs against PSG, analyzing the influence of study design and device characteristics. The results revealed moderate to substantial agreement between wEEGs and PSG, with performance varying across sleep stages. The N1 stage posed significant classification challenges, while N3 (Deep Sleep) was most reliably detected. Manually scored wEEG data outperformed automatic scoring for N1 detection, and a higher electrode count was associated with improved N3 classification. This study proposes a standardized framework with balanced metrics like MCC and κ to address stage-specific performance variabilities, enhancing device comparability. The findings highlight the strengths and weaknesses of wEEGs and guide future research to refine automatic staging, contributing to their optimization for clinical and consumer applications.
Identifiers
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Registered trials
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