Nonconvulsive Epileptic Seizure Detection in Scalp EEG Using Multiway Data Analysis

Yissel Rodríguez Aldana*, Borbála Hunyadi, Enrique J.Marañón Reyes, Valia Rodríguez Rodríguez, Sabine Van Huffel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally prolonged epileptic seizures without evident physical symptoms. Since these continuous seizures may cause permanent brain damage, it constitutes a medical emergency. This paper proposes a method to detect nonconvulsive seizures for a further nonconvulsive status epilepticus diagnosis. To differentiate between the normal and seizure electroencephalogram (EEG), a K-Nearest Neighbor, a Radial Basis Support Vector Machine, and a Linear Discriminant Analysis classifier are used. The classifier features are obtained from the Canonical Polyadic Decomposition (CPD) and Block Term Decomposition (BTD) of the EEG data represented as third order tensor. To expand the EEG into a tensor, Wavelet or Hilbert-Huang transform are used. The algorithm is tested on a scalp EEG database of 139 seizures of different duration. The experimental results suggest that a Hilbert-Huang tensor representation and the CPD analysis provide the most suitable framework for nonconvulsive seizure detection. The Radial Basis Support Vector Machine classifier shows the best performance with sensitivity, specificity, and accuracy values over 98%. A rough comparison with other methods proposed in the literature shows the superior performance of the proposed method for nonconvulsive epileptic seizure detection.

Original languageEnglish
Article number8351633
Pages (from-to)660-671
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number2
Early online date27 Apr 2018
Publication statusPublished - 1 Mar 2019

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  • Hilbert Huang Transform
  • Multiway Data Analysis
  • Nonconvulsive epileptic seizures
  • Wavelet Transform


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