Performance metrics for online seizure prediction

Hsiang Han Chen, Vladimir Cherkassky

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Many recent studies on online seizure prediction from iEEG signal describe various prediction algorithms and their prediction performance. In contrast, this paper focuses on proper specification of system parameters, such as prediction period, prediction horizon and data-driven characterization of lead seizures. Whereas prediction performance clearly depends on these system parameters many researchers simply set the values of these parameters in an ad hoc manner. Our paper investigates the effect of these system parameters on online prediction performance, using both synthetic and real-life data sets. Therefore, meaningful comparison of methods/algorithms (for online seizure prediction) should consider proper specification of system parameters.

Original languageEnglish (US)
Pages (from-to)22-32
Number of pages11
JournalNeural Networks
Volume128
DOIs
StatePublished - Aug 2020

Bibliographical note

Funding Information:
This work was supported by NIH grant UH2NS095495 , and by NIH grant R01NS092882 .

Publisher Copyright:
© 2020 Elsevier Ltd

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

Keywords

  • Lead seizure
  • Online seizure prediction
  • Prediction horizon
  • Prediction period
  • Sensitivity
  • iEEG signal

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