Literature DB >> 29873826

Seizure detection using scalp-EEG.

Christoph Baumgartner1,2,3, Johannes P Koren2,3.   

Abstract

Scalp electroencephalography (EEG)-based seizure-detection algorithms applied in a clinical setting should detect a broad range of different seizures with high sensitivity and selectivity and should be easy to use with identical parameter settings for all patients. Available algorithms provide sensitivities between 75% and 90%. EEG seizure patterns with short duration, low amplitude, circumscribed focal activity, high frequency, and unusual morphology as well as EEG seizure patterns obscured by artifacts are generally difficult to detect. Therefore, detection algorithms generally perform worse on seizures of extratemporal origin as compared to those of temporal lobe origin. Specificity (false-positive alarms) varies between 0.1 and 5 per hour. Low false-positive alarm rates are of critical importance for acceptance of algorithms in a clinical setting. Reasons for false-positive alarms include physiological and pathological interictal EEG activities as well as various artifacts. To achieve a stable, reproducible performance (especially concerning specificity), algorithms need to be tested and validated on a large amount of EEG data comprising a complete temporal assessment of all interictal EEG. Patient-specific algorithms can further improve sensitivity and specificity but need parameter adjustments and training for individual patients. Seizure alarm systems need to provide on-line calculation with short detection delays in the order of few seconds. Scalp-EEG-based seizure detection systems can be helpful in an everyday clinical setting in the epilepsy monitoring unit, but at the current stage cannot replace continuous supervision of patients and complete visual review of the acquired data by specially trained personnel. In an outpatient setting, application of scalp-EEG-based seizure-detection systems is limited because patients won't tolerate wearing widespread EEG electrode arrays for long periods in everyday life. Recently developed subcutaneous EEG electrodes may offer a solution in this respect. Wiley Periodicals, Inc.
© 2018 International League Against Epilepsy.

Entities:  

Keywords:  detection delay; epilepsy monitoring unit; false alarm rate; sensitivity

Mesh:

Year:  2018        PMID: 29873826     DOI: 10.1111/epi.14052

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


  18 in total

1.  Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

Authors:  Andreas Bahr; Matthias Schneider; Maria Avitha Francis; Hendrik M Lehmann; Igor Barg; Anna-Sophia Buschhoff; Peer Wulff; Thomas Strunskus; Franz Faupel
Journal:  Biosensors (Basel)       Date:  2021-06-23

Review 2.  Dogs as a Natural Animal Model of Epilepsy.

Authors:  Wolfgang Löscher
Journal:  Front Vet Sci       Date:  2022-06-22

3.  Ultra-long-term subcutaneous home monitoring of epilepsy-490 days of EEG from nine patients.

Authors:  Sigge Weisdorf; Jonas Duun-Henriksen; Marianne J Kjeldsen; Frantz R Poulsen; Sirin W Gangstad; Troels W Kjaer
Journal:  Epilepsia       Date:  2019-10-13       Impact factor: 5.864

4.  Integrating old and new complexity measures toward automated seizure detection from long-term video EEG recordings.

Authors:  Manuel Ruiz Marín; Irene Villegas Martínez; Germán Rodríguez Bermúdez; Maurizio Porfiri
Journal:  iScience       Date:  2020-12-28

5.  Classification with a Deferral Option and Low-Trust Filtering for Automated Seizure Detection.

Authors:  Thijs Becker; Kaat Vandecasteele; Christos Chatzichristos; Wim Van Paesschen; Dirk Valkenborg; Sabine Van Huffel; Maarten De Vos
Journal:  Sensors (Basel)       Date:  2021-02-04       Impact factor: 3.576

6.  Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings.

Authors:  Michael L Martini; Aly A Valliani; Claire Sun; Anthony B Costa; Shan Zhao; Fedor Panov; Saadi Ghatan; Kanaka Rajan; Eric Karl Oermann
Journal:  Sci Rep       Date:  2021-04-05       Impact factor: 4.379

7.  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

8.  Accurate detection of spontaneous seizures using a generalized linear model with external validation.

Authors:  Nicolas F Fumeaux; Senan Ebrahim; Brian F Coughlin; Adesh Kadambi; Aafreen Azmi; Jen X Xu; Maurice Abou Jaoude; Sunil B Nagaraj; Kyle E Thomson; Thomas G Newell; Cameron S Metcalf; Karen S Wilcox; Eyal Y Kimchi; Marcio F D Moraes; Sydney S Cash
Journal:  Epilepsia       Date:  2020-08-06       Impact factor: 6.740

9.  Automatic sleep stage classification based on subcutaneous EEG in patients with epilepsy.

Authors:  Sirin W Gangstad; Kaare B Mikkelsen; Preben Kidmose; Yousef R Tabar; Sigge Weisdorf; Maja H Lauritzen; Martin C Hemmsen; Lars K Hansen; Troels W Kjaer; Jonas Duun-Henriksen
Journal:  Biomed Eng Online       Date:  2019-10-30       Impact factor: 2.819

Review 10.  Noninvasive detection of focal seizures in ambulatory patients.

Authors:  Philippe Ryvlin; Leila Cammoun; Ilona Hubbard; France Ravey; Sandor Beniczky; David Atienza
Journal:  Epilepsia       Date:  2020-06-02       Impact factor: 5.864

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