Evidence map›Paper›PMID 40265999›Full record

ArticleEpilepsia2025

Deep learning-based detection of generalized convulsive seizures using a wrist-worn accelerometer.

Antoine Spahr, Adriano Bernini, Pauline Ducouret, Christoph Baumgartner, Johannes P Koren, Lukas Imbach, Sàndor Beniczky, Sidsel A Larsen, Sylvain Rheims, Martin Fabricius and 7 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Epilepsia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–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

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
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

17 authors.

Antoine SpahrNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.
Adriano BerniniNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.ORCID 0000-0003-0207-0064
Pauline DucouretNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.
Christoph BaumgartnerDepartment of Neurology, Clinic Hietzing, Vienna, Austria.
Johannes P KorenDepartment of Neurology, Clinic Hietzing, Vienna, Austria.ORCID 0000-0002-5290-2837
Lukas ImbachSwiss Epilepsy Center, Klinik Lengg, Zurich, Switzerland.ORCID 0000-0002-6135-8642
Sàndor BeniczkyDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.ORCID 0000-0002-6035-6581
Sidsel A LarsenDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.
Sylvain RheimsDepartment of Functional Neurology and Epileptology, Hospices Civils de Lyon, Lyon 1 University, Lyon, France.ORCID 0000-0002-4663-8515
Martin FabriciusDepartment of Clinical Neurophysiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.ORCID 0000-0002-6959-6848
Margitta SeeckEEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.
Berhard J SteinhoffEpilepsiezentrum Kork, Kehl-Kork, Germany.ORCID 0000-0001-5995-5862
Isabelle BeuchatNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.ORCID 0000-0002-9300-4443
Jonathan DanEmbedded Systems Laboratory, EPFL, Lausanne, Switzerland.ORCID 0000-0002-2338-572X
David A AtienzaEmbedded Systems Laboratory, EPFL, Lausanne, Switzerland.
Charles-Edouard BardynNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.
Philippe RyvlinNeuroDigital@NeuroTech, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne, Lausanne, Switzerland.ORCID 0000-0001-7775-6576

Funding

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10.002.812Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 180365Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 320030_179240Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung CRSII5_193813
6 · The paper itself

Abstract

objectiveTo develop and validate a wrist-worn accelerometer-based, deep-learning tunable algorithm for the automated detection of generalized or bilateral convulsive seizures (CSs) to be integrated with off-the-shelf smartwatches.

methodsWe conducted a prospective multi-center study across eight European epilepsy monitoring units, collecting data from 384 patients undergoing video electroencephalography (vEEG) monitoring with a wrist-worn three dimensional (3D)-accelerometer sensor. We developed an ensemble-based convolutional neural network architecture with tunable sensitivity through quantile-based aggregation. The model, referred to as Episave, used accelerometer amplitude as input. It was trained on data from 37 patients who had 54 CSs and evaluated on an independent dataset comprising 347 patients, including 33 who had 49 CSs.

resultsCross-validation on the training set showed that optimal performance was obtained with an aggregation quantile of 60, with a 98% sensitivity, and a false alarm rate (FAR) of 1/6 days. Using this quantile on the independent test set, the model achieved a 96% sensitivity (95% confidence interval [CI]: 90%-100%), a FAR of <1/8 days (95% CI: 1/9-1/7 days) with 1 FA/61 nights, and a median detection latency of 26 s. One of the two missed CSs could be explained by the patient's arm, which was wearing the sensor, being trapped in the bed rail. Other quantiles provided up to 100% sensitivity at the cost of a greater FAR (1/2 days) or very low FAR (1/100 days) at the cost of lower sensitivity (86%). SIGNIFICANCE: This Phase 2 clinical validation study suggests that deep learning techniques applied to single-sensor accelerometer data can achieve high CS detection performance while enabling tunable sensitivity.

Indexed as

AccelerometryDeep LearningEpilepsy, GeneralizedSeizuresWearable Electronic DevicesAdolescentAdultAlgorithmsChildElectroencephalographyFemaleHumansMaleMiddle AgedNeural Networks, ComputerProspective Studiesdeep learningepilepsyfocal‐to‐bilateral tonic–clonic seizuresgeneralized tonic–clonic seizureseizure detectionwearable

Identifiers

PMID40265999
PMCPMC12489714

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.