Automatic recognition of epileptiform EEG abnormalities

Alexander Brenner, Ekaterina Kutafina, Stephan M. Jonas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations


Long term EEG examinations, for example during epilepsy diagnosis, can be performed more efficiently with support of automated abnormality detection. Currently, these methods are usually developed based on one specific database, which limits the possibilities of generalizations. Here, we present a machine learning solution for detection of interictal abnormal EEG segments optimized on the publically available TUH Abnormal EEG Corpus. The classifier is further re-trained and tested on several combinations of publicly available data sets. The results achieved internally on the datasets are comparable to the known state of the art, while training and testing on different sources produced accuracy in the range of 67.51% to 99.50%. Lower accuracy is achieved when the training data set is highly preprocessed and relatively small.

Original languageEnglish
Title of host publicationBuilding Continents of Knowledge in Oceans of Data
Subtitle of host publicationThe Future of Co-Created eHealth - Proceedings of MIE 2018
EditorsAdrien Ugon, Daniel Karlsson, Gunnar O. Klein, Anne Moen
PublisherIOS Press BV
Number of pages5
ISBN (Electronic)9781614998518
StatePublished - 2018
Externally publishedYes
Event40th Medical Informatics in Europe Conference, MIE 2018 - Gothenburg, Sweden
Duration: 24 Apr 201826 Apr 2018

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


Conference40th Medical Informatics in Europe Conference, MIE 2018


  • EEG
  • Epilepsy
  • Interictal abnormality
  • Spike detection


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