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April 02, 2019; 92 (14) Article

Automated seizure detection accuracy for ambulatory EEG recordings

Karina A. González Otárula, Yara Mikhaeil-Demo, Elizabeth M. Bachman, Pedro Balaguera, Stephan Schuele
First published March 6, 2019, DOI: https://doi.org/10.1212/WNL.0000000000007237
Karina A. González Otárula
From the Department of Neurology (K.A.G.O., Y.M.-D., E.M.B., S.S.), Northwestern University, Chicago, IL; and Department of Neurology (P.B.), Baylor College of Medicine, Houston, TX.
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Yara Mikhaeil-Demo
From the Department of Neurology (K.A.G.O., Y.M.-D., E.M.B., S.S.), Northwestern University, Chicago, IL; and Department of Neurology (P.B.), Baylor College of Medicine, Houston, TX.
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Elizabeth M. Bachman
From the Department of Neurology (K.A.G.O., Y.M.-D., E.M.B., S.S.), Northwestern University, Chicago, IL; and Department of Neurology (P.B.), Baylor College of Medicine, Houston, TX.
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Pedro Balaguera
From the Department of Neurology (K.A.G.O., Y.M.-D., E.M.B., S.S.), Northwestern University, Chicago, IL; and Department of Neurology (P.B.), Baylor College of Medicine, Houston, TX.
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Stephan Schuele
From the Department of Neurology (K.A.G.O., Y.M.-D., E.M.B., S.S.), Northwestern University, Chicago, IL; and Department of Neurology (P.B.), Baylor College of Medicine, Houston, TX.
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Automated seizure detection accuracy for ambulatory EEG recordings
Karina A. González Otárula, Yara Mikhaeil-Demo, Elizabeth M. Bachman, Pedro Balaguera, Stephan Schuele
Neurology Apr 2019, 92 (14) e1540-e1546; DOI: 10.1212/WNL.0000000000007237

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Abstract

Objective To investigate the accuracy of preselected software automatic seizure files to detect at least one seizure per study in prolonged ambulatory EEG recording.

Methods All the prolonged ambulatory EEG recordings (>24 hours) read at the Northwestern Memorial Hospital from January 2013 to October 2017 were included. We selected only the first study of each patient. We reviewed the studies entirely, and processed the recordings through 1 of 3 different detection software that are commercially available (Persyst 11, Persyst 12, and Gotman TM Event Detection). The proportion of patients with at least one electrographic seizure (≥10 seconds) correctly identified by a seizure detector was calculated. Finally, we evaluated whether the type of seizure (focal vs generalized) may affect the chances of being automatically detected.

Results We read 1,478 ambulatory EEG studies entirely (2,323 days of EEG recording; average 1.6 d/study). From the first study of each patient (1,257 studies), we found electrographic seizures in 70 (5.6%) studies. In 37 of 70 patients (53%), the automatic detectors correctly identified at least one seizure. Detections happened slightly more frequently in generalized seizures (14/20, 70%) compared to focal seizures (23/50, 46%) (p = 0.06).

Conclusion Seizures were found in 5.6% of the studies. Automatic seizure detectors identified at least one electrographic seizure in only 53% of the studies. They performed slightly better detecting generalized than focal seizures. Therefore, the review of only automatically selected segments may be of decreased value to identify seizures, in particular when focal seizures are suspected.

Glossary

aEEG=
ambulatory EEG;
ASD=
automatic seizure detector

Footnotes

  • Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Editorial, page 641

  • Podcast: NPub.org/axal9n

  • CME Course: NPub.org/cmelist

  • Received March 19, 2018.
  • Accepted in final form November 24, 2018.
  • © 2019 American Academy of Neurology
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Letters: Rapid online correspondence

  • Teaching an EEG to read itself
    • Nitin K. Sethi, Associate Professor of Neurology, New York-Presbyterian Hospital, Weill Cornell Medical Center (New York, NY)
    Submitted March 08, 2019
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