Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection

J. G. Bogaarts*, Danny Hilkman, Erik Gommer, Vivianne van Kranen - Mastenbroek, J.R. Reulen

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Continuous electroencephalographic monitoring of critically ill patients is an established procedure in intensive care units. Seizure detection algorithms, such as support vector machines (SVM), play a prominent role in this procedure. To correct for inter-human differences in EEG characteristics, as well as for intra-human EEG variability over time, dynamic EEG feature normalization is essential. Recently, the median decaying memory (MDM) approach was determined to be the best method of normalization. MDM uses a sliding baseline buffer of EEG epochs to calculate feature normalization constants. However, while this method does include non-seizure EEG epochs, it also includes EEG activity that can have a detrimental effect on the normalization and subsequent seizure detection performance. In this study, EEG data that is to be incorporated into the baseline buffer are automatically selected based on a novelty detection algorithm (Novelty-MDM). Performance of an SVM-based seizure detection framework is evaluated in 17 long-term ICU registrations using the area under the sensitivity-specificity ROC curve. This evaluation compares three different EEG normalization methods, namely a fixed baseline buffer (FB), the median decaying memory (MDM) approach, and our novelty median decaying memory (Novelty-MDM) method. It is demonstrated that MDM did not improve overall performance compared to FB (p <0.27), partly because seizure like episodes were included in the baseline. More importantly, Novelty-MDM significantly outperforms both FB (p = 0.015) and MDM (p = 0.0065).

Original languageEnglish
Pages (from-to)1883-1892
Number of pages10
JournalMedical & Biological Engineering & Computing
Issue number12
Publication statusPublished - Dec 2016


  • Seizure detection
  • SVM
  • PCA
  • EEG
  • Feature normalization


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