ArticleSleep2026
Automated deep learning-based arousal detection agrees with ten expert scorers in unseen data.
Article in Sleep, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
STUDY
objectivesAutomatic arousal scoring is essential for the cost-effective analysis of polysomnograms. Many previous automatic methods have been tested in homogeneous, single-center datasets, and all against one scorer, on event-by-event basis, or in 30-s segments. We developed a deep learning-based arousal scoring model and tested it at 1-s resolution in an independent dataset scored by ten expert scorers from seven different sleep centers. MATERIALS AND
methodsA fully convolutional neural network model was developed using 1847 openly available polysomnograms from the Multi-Ethnic Study of Atherosclerosis (MESA) and tested in 205 hold-out polysomnograms from the MESA cohort, and in an independent Sleep Revolution (SR) cohort of 50 polysomnograms, each scored by ten scorers. Model performance was evaluated on an event-by-event basis and in 1-s segments. In the SR dataset, the performance was tested against each scorer individually and against their majority agreement.
resultsIn the MESA test set, the model achieved an event-by-event F1-score of 0.77, a 1-s segment κ-value of 0.67, and an arousal index (ArI) intraclass correlation coefficient (ICC) of 0.83. In the multi-scorer SR dataset, the model reached a median event-by-event F1-score, a 1-s κ-value, and an ArI ICC of 0.61, 0.50, and 0.66, respectively, exceeding the corresponding median values of the scorers among themselves (0.57, 0.46, and 0.59, respectively).
conclusionsThe model achieved higher median arousal scoring agreement with ten manual scorers than the scorers did among themselves in an independent dataset. This suggests that the model generalizes well and can reliably be used in different sleep recordings.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.