Nonconvulsive epileptic seizure monitoring with incremental learning

Yissel Rodríguez Aldana*, Enrique J. Marañón Reyes, Frank Sanabria Macias, Valia Rodríguez Rodríguez, Lilia Morales Chacón, Sabine Van Huffel, Borbála Hunyadi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accurately detected NCSz in EEG data (referred here as ‘Batch method’). However, this approach was less effective when the EEG features identified at the beginning of the recording changed over time. Such pattern drift is an issue that causes failures of automated seizure detection methods. This paper presents a support vector machine (SVM)-based incremental learning method for NCSz detection that for the first time addresses the seizure evolution in EEG records from patients with epileptic disorders and from ICU having NCSz. To implement the incremental learning SVM, three methodologies are tested. These approaches differ in the way they reduce the set of potentially available support vectors that are used to build the decision function of the classifier. To evaluate the suitability of the three incremental learning approaches proposed here for NCSz detection, first, a comparative study between the three methods is performed. Secondly, the incremental learning approach with the best performance is compared with the Batch method and three other batch methods from the literature. From this comparison, the incremental learning method based on maximum relevance minimum redundancy (MRMR_IL) obtained the best results. MRMR_IL method proved to be an effective tool for NCSz detection in a real-time setting, achieving sensitivity and accuracy values above 99%.
Original languageEnglish
Article number103434
JournalComputers in Biology and Medicine
Early online date6 Sept 2019
Publication statusPublished - 1 Nov 2019

Bibliographical note

© 2019, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International

Funding: Belgian foreign Affairs-Development Cooperation through VLIR-UOS (2013–2019) (Flemish Interuniversity Council-University Cooperation for Development); imec funds 2017 and the European Research Council under the European Union's Seventh Framework Programme (FP7/2007–2013)/ERC Advanced Grant: BIOTENSORS (no.339804).


  • Hilbert huang transform
  • Incremental learning
  • Multiway data analysis
  • Nonconvulsive epileptic seizures


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