Deep Learning for EEG Analysis

Catarina da Silva Lourenço

Research output: ThesisPhD Thesis - Research UT, graduation UT

125 Downloads (Pure)


The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis of the EEG.
Visual assessment is, however, time consuming and prone to subjectivity, leading to a high misdiagnosis rate and motivating the development of automated approaches. Research towards automating IED detection started 45 years ago. Approaches range from mimetic methods to deep learning techniques. We review different approaches to IED detection, discussing their performance and limitations. Traditional machine learning and
deep learning methods have yielded the best results so far and their application in the field is still growing. Standardization of datasets and outcome measures is necessary to compare models more objectively and decide which should be implemented in a clinical setting.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • University of Twente
  • van Putten, Michel J.A.M., Supervisor
  • Tjepkema, Marleen Catharina, Co-Supervisor
Award date14 Jul 2023
Place of PublicationEnschede
Print ISBNs978-90-365-5690-3
Electronic ISBNs978-90-365-5691-0
Publication statusPublished - 14 Jul 2023


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