Evidence map›Paper›PMID 41507468›Full record

ArticleNPJ digital medicine2026

Automated video-based differentiation of sleep-related hypermotor epilepsy and parasomnia episodes.

Matteo Moro, Federica Sassi, Ramona Cordani, Anna Castelnovo, Mauro Manconi, Paola Proserpio, Laura Tassi, Federica Provini, Francesca Odone, Maura Casadio and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Matteo MoroDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy. matteo.moro@unige.it.
Federica SassiDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
Ramona CordaniDipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), University of Genova, Genova, Italy.
Anna CastelnovoNeurocenter of Italian Switzerland, Ente Ospedaliero Cantonale, Ospedale Civico, Lugano, Switzerland.
Mauro ManconiNeurocenter of Italian Switzerland, Ente Ospedaliero Cantonale, Ospedale Civico, Lugano, Switzerland.
Paola ProserpioIRCCS San Raffaele Hospital, Milan, Italy.
Laura Tassi"C. Munari" Epilepsy Surgery Center, Niguarda Hospital, Milan, Italy.
Federica ProviniDepartment of Biomedical and Neuromotor Sciences, Bellaria Hospital, University of Bologna, Bologna, Italy.
Francesca OdoneDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
Maura CasadioDepartment of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, Genova, Italy.
Lino NobiliDipartimento di Neuroscienze, Riabilitazione, Oftalmologia, Genetica e Scienze Materno-Infantili (DINOGMI), University of Genova, Genova, Italy.
VISTA group

Funding

European Union - NextGenerationEU "MNESYS - A Multiscale Integrated Approach to the Study of the Nervous System in Health and Disease" (PE0000006; DN. 1553 11.10.2022)European Union - NextGenerationEU "RAISE - Robotics and AI for Socio-economic Empowerment" (ECS00000035)Ministero della Salute 5 x 1000 project 2017, "SOLAR: Sleep disorders in children: an innovative clinical research perspective"
6 · The paper itself

Abstract

Distinguishing epileptic seizures from parasomnias is challenging due to overlapping motor features. This study evaluated a SlowFast deep learning model using video recordings of 167 individuals to classify Sleep-Related Hypermotor Epilepsy, Disorders of Arousal, and REM Sleep Behavior Disorder. The model achieved a mean accuracy of 83.3% across three data splits. This work represents an initial step toward developing automated tools to support clinicians in assessing sleep-related motor events.

Identifiers

PMID41507468
PMCPMC12891581

What OpenQuestion holds

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Registered trials

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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.