Epileptic seizure onset predicts its duration

Yueyang Liu, Zhinoos Razavi Hesabi, Mark Cook, Levin Kuhlmann

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Background: Epilepsy is characterized by recurrent seizures that have a variety of manifestations. The severity of, and risks for patients associated with, seizures are largely linked to the duration of seizures. Methods that determine seizure duration based on seizure onsets could be used to help mitigate the risks associated with what might be extended seizures by guiding timely interventions. Methods: Using long-term intracranial electroencephalography (iEEG) recordings, this article presents a method for predicting whether a seizure is going to be long or short by analyzing the seizure onset. The definition of long and short depends on each patient's seizure distribution. By analyzing 2954 seizures from 10 patients, patient-specific classifiers were built to predict seizure duration given the first few seconds from the onset. Results: The proposed methodology achieved an average area under the receiver operating characteristic curve (AUC) performance of 0.7 for the 5 of 10 patients with above chance prediction performance (p value from 0.04 to 10−9). Conclusions: Our results imply that the duration of seizures can be predicted from the onset in some patients. This could form the basis of methods for predicting status epilepticus or optimizing the amount of electrical stimulation delivered by seizure control devices.

Original languageEnglish
Pages (from-to)375-381
Number of pages7
JournalEuropean Journal of Neurology
Volume29
Issue number2
DOIs
Publication statusPublished - Feb 2022

Keywords

  • dynamic labelling
  • gradient boosting
  • machine learning
  • seizure duration prediction

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