Literature DB >> 33666944

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

Sándor Beniczky1,2, Samuel Wiebe3, Jesper Jeppesen4, William O Tatum5, Milan Brazdil6,7, Yuping Wang8, Susan T Herman9, Philippe Ryvlin10.   

Abstract

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.
© 2021 International League Against Epilepsy.

Entities:  

Keywords:  algorithms; automated detection; epilepsy; seizure detection; wearable devices

Year:  2021        PMID: 33666944     DOI: 10.1111/epi.16818

Source DB:  PubMed          Journal:  Epilepsia        ISSN: 0013-9580            Impact factor:   5.864


  7 in total

1.  Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.

Authors:  Sebastian Böttcher; Elisa Bruno; Nino Epitashvili; Matthias Dümpelmann; Nicolas Zabler; Martin Glasstetter; Valentina Ticcinelli; Sarah Thorpe; Simon Lees; Kristof Van Laerhoven; Mark P Richardson; Andreas Schulze-Bonhage
Journal:  Sensors (Basel)       Date:  2022-04-26       Impact factor: 3.847

2.  The Individual Ictal Fingerprint: Combining Movement Measures With Ultra Long-Term Subcutaneous EEG in People With Epilepsy.

Authors:  Troels W Kjaer; Line S Remvig; Asbjoern W Helge; Jonas Duun-Henriksen
Journal:  Front Neurol       Date:  2021-12-23       Impact factor: 4.003

3.  Parental preferences for seizure detection devices: A discrete choice experiment.

Authors:  Anouk van Westrhenen; Ben F M Wijnen; Roland D Thijs
Journal:  Epilepsia       Date:  2022-03-04       Impact factor: 6.740

4.  Automated detection of nocturnal motor seizures using an audio-video system.

Authors:  Sidsel Armand Larsen; Daniella Terney; Tim Østerkjerhuus; Torsten Vinding Merinder; Kaapo Annala; Andrew Knight; Sándor Beniczky
Journal:  Brain Behav       Date:  2022-08-08       Impact factor: 3.405

5.  Multimodal nocturnal seizure detection: Do we need to adapt algorithms for children?

Authors:  Richard H C Lazeron; Roland D Thijs; Johan Arends; Thea Gutter; Pierre Cluitmans; Johannes Van Dijk; Francis I Y Tan; Wytske Hofstra; Claire E H M Donjacour; Frans Leijten
Journal:  Epilepsia Open       Date:  2022-07-21

6.  Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis.

Authors:  Vaidehi Naganur; Shobi Sivathamboo; Zhibin Chen; Shitanshu Kusmakar; Ana Antonic-Baker; Terence J O'Brien; Patrick Kwan
Journal:  Epilepsia       Date:  2022-05-28       Impact factor: 6.740

7.  A neuromorphic spiking neural network detects epileptic high frequency oscillations in the scalp EEG.

Authors:  Karla Burelo; Georgia Ramantani; Giacomo Indiveri; Johannes Sarnthein
Journal:  Sci Rep       Date:  2022-02-02       Impact factor: 4.996

  7 in total

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