Evidence map›Paper›PMID 41027311›Full record

Trial reportEBioMedicine2025

Seizure detection using wearable electrocardiogram connected to a smartphone: a phase 3 clinical validation study.

Jesper Jeppesen, Jakob Christensen, Oliver Ahrenfeldt Petersen, Sarah Fenger, Sidsel Armand Larsen, Stephan Wüstenhagen, Stefan Rahr Wagner, Peter Johansen, Sándor Beniczky

Abstract readClinical Trial, Phase IIIMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in EBioMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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

9 authors.

Jesper JeppesenDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Jakob ChristensenDepartment of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Neurology, Aarhus University Hospital, Aarhus, Denmark.
Oliver Ahrenfeldt PetersenDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Sarah FengerDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Sidsel Armand LarsenDepartment of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
Stephan WüstenhagenDepartment of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.
Stefan Rahr WagnerDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Peter JohansenDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.
Sándor BeniczkyDepartment of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark. Electronic address: sbz@filadelfia.dk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutomated seizure detection is needed for patient safety and for objective seizure quantification. Wearable seizure detection devices hold great potential to improve patient care. Our objectives were to assess the accuracy of a wearable ECG-device connected to a smartphone, in detecting epileptic seizures in patients with autonomic ictal changes, and evaluate its capability to automatically determine impairment of consciousness.

methodsWe conducted a phase 3, prospective, blinded, multicentre, clinical validation study of real-time seizure detection using a predefined algorithm. We recruited consecutive patients admitted to Epilepsy Monitoring Units. Eligible patients experienced seizures with autonomic ictal manifestations, defined as ictal heart rate change exceeding 50 beats per minute, inferred from the first recorded seizure. Patients wore an ECG-device connected to a smartphone. The algorithm, based on heart rate variability, used a personalised detection threshold determined from the first 24 h of recording. During daytime, seizure detection triggered automated behavioural-testing on the smartphone to confirm detection and assess consciousness.

findingsOf 101 enrolled patients, 36 experienced seizures, with 42 seizures recorded from 17 eligible patients. Overall sensitivity across all 42 seizures was 90·5% (95% CI: 77·4-97·3%), median sensitivity per patient was 100% (95% CI: 100-100%). All bilateral tonic-clonic seizures were detected, while sensitivity for other focal seizures was 82·6% (95% CI: 61·2-95·1%), median per patient: 100% (95% CI: 60-100%). Mean false alarm rate was 2·5/day (median per patient: 1·1/day, 95% CI: 0-2·8/day, zero during the night). Device deficiency time was 1·8% and signal loss was 4·5% (median per patient: 0·3% and 0·5% respectively). Use of the behavioural-testing application successfully cancelled all false alarms and accurately identified impairment of consciousness.

interpretationThe wearable ECG device connected to a smartphone accurately detected focal and generalised seizures, and assessed impairment of consciousness.

fundingIndependent Research Fund Denmark (grant number 0134-00400B).

Indexed as

ElectrocardiographySeizuresSmartphoneWearable Electronic DevicesAdolescentAdultAgedAlgorithmsFemaleHeart RateHumansMaleMiddle AgedProspective StudiesYoung AdultAutomated behavioural testingAutomated seizure detectionElectrocardiographyFocal seizuresHeart rate variabilityTonic-clonic seizures

Identifiers

PMID41027311
PMCPMC12516532

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.