Evidence map›Paper›PMID 32160324›Full record

ArticleEpilepsia2020

Visual seizure annotation and automated seizure detection using behind-the-ear electroencephalographic channels.

Kaat Vandecasteele, Thomas De Cooman, Jonathan Dan, Evy Cleeren, Sabine Van Huffel, Borbála Hunyadi, Wim Van Paesschen

2 registry-linked trialsAbstract read
In one paragraph

Article in Epilepsia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing 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.

NCT04642105 nacompletednot on this mapstarted 2021, after this paper: background citation

Multimodal Profiling of People With Epilepsy to Determine Which Signals Are Clinically Useful for Long-term Home Monitoring

TypeinterventionalSponsorUniversitaire Ziekenhuizen KU LeuvenRan2021 to 2024Enrolled54ConditionsEpilepsyArmsSensor-Dot and Plug 'n Patch system
NCT05738226 nawithdrawnnot on this mapstarted 2023, after this paper: background citation

Validation Study for Monitoring of Focal Onset Epileptic Seizures With a Wearable Seizure Monitoring Device, EpiCare@Home

TypeinterventionalSponsorBytefliesRan2023 to 2023Enrolled0ConditionsEpilepsy, Seizures, Seizures, Focal, Seizure, RefractoryArmsEpiCare@Home, wearable multimodal seizure monitoring system
3 · Its place in the literature

Who cites it

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

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Seizure Detection Devices.Journal of clinical medicine · 2025
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. Digital Health Technologies in Pediatric Trials.Therapeutic innovation & regulatory science · 2022
    Review
  18. Article
  19. Article
  20. [Research progress on wearable physiological parameter monitoring and its clinical applications].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021
    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

7 authors.

Kaat VandecasteeleDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.ORCID 0000-0002-9888-577X
Thomas De CoomanDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Jonathan DanDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Evy CleerenDepartment of Neurology, UZ Leuven, Leuven, Belgium.
Sabine Van HuffelDepartment of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Borbála HunyadiDepartment of Microelectronics, Delft University of Technology, Delft, the Netherlands.
Wim Van PaesschenDepartment of Neurology, UZ Leuven, Leuven, Belgium.ORCID 0000-0002-8535-1699

Funding

EIT 19263 SeizeIT2
6 · The paper itself

Abstract

objectiveSeizure diaries kept by patients are unreliable. Automated electroencephalography (EEG)-based seizure detection systems are a useful support tool to objectively detect and register seizures during long-term video-EEG recording. However, this standard full scalp-EEG recording setup is of limited use outside the hospital, and a discreet, wearable device is needed for capturing seizures in the home setting. We are developing a wearable device that records EEG with behind-the-ear electrodes. In this study, we determined whether the recognition of ictal patterns using only behind-the-ear EEG channels is possible. Second, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels.

methodsFifty-four patients with a total of 182 seizures, mostly temporal lobe epilepsy (TLE), and 5284 hours of data, were recorded with a standard video-EEG at University Hospital Leuven. In addition, extra behind-the-ear EEG channels were recorded. First, a neurologist was asked to annotate behind-the-ear EEG segments containing selected seizure and nonseizure fragments. Second, a data-driven algorithm was developed using only behind-the-ear EEG. This algorithm was trained using data from other patients (patient-independent model) or from the same patient (patient-specific model).

resultsThe visual recognition study resulted in 65.7% sensitivity and 94.4% specificity. By using those seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false-positive detections (FPs)/24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 FPs/24 hours. SIGNIFICANCE: Visual recognition of ictal EEG patterns using only behind-the-ear EEG is possible in a significant number of patients with TLE. A patient-specific seizure detection algorithm using only behind-the-ear EEG was able to detect more seizures automatically than what patients typically report, with 0.49 FPs/24 hours. We conclude that a large number of refractory TLE patients can benefit from using this device.

Indexed as

AlgorithmsSignal Processing, Computer-AssistedElectrodesElectroencephalographyEpilepsy, Temporal LobeFemaleHumansMaleSeizuresSensitivity and SpecificityWearable Electronic Devicesautomated algorithmsbehind-the-ear EEGepilepsyreduced electrode montageseizure detectionwearable sensors

Identifiers

PMID32160324
PMCPMC7217054

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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 OpenQuestion graph.