Fast statistical model-based classification of epileptic EEG signals

Antonio Quintero-Rincón, Marcelo Pereyra, Carlos D'Giano, Marcelo Risk, Hadj Batatia

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

This paper presents a supervised classification method to accurately detect epileptic brain activity in real-time from electroencephalography (EEG) data. The proposed method has three main strengths: it has low computational cost, making it suitable for real-time implementation in EEG devices; it performs detection separately for each brain rhythm or EEG spectral band, following the current medical practices; and it can be trained with small datasets, which is key in clinical problems where there is limited annotated data available. This is in sharp contrast with modern approaches based on machine learning techniques, which achieve very high sensitivity and specificity but require large training sets with expert annotations that may not be available. The proposed method proceeds by first separating EEG signals into their five brain rhythms by using a wavelet filter bank. Each brain rhythm signal is then mapped to a low-dimensional manifold by using a generalized Gaussian statistical model; this dimensionality reduction step is computationally straightforward and greatly improves supervised classification performance in problems with little training data available. Finally, this is followed by parallel linear classifications on the statistical manifold to detect if the signals exhibit healthy or abnormal brain activity in each spectral band. The good performance of the proposed method is demonstrated with an application to paediatric neurology using 39 EEG recordings from the Children's Hospital Boston database, where it achieves an average sensitivity of 98%, specificity of 88%, and detection latency of 4 s, performing similarly to the best approaches from the literature.

Original languageEnglish
Pages (from-to)877-889
Number of pages13
JournalBiocybernetics and Biomedical Engineering
Volume38
Issue number4
Early online date21 Aug 2018
DOIs
Publication statusPublished - 2018

Keywords

  • EEG
  • Epilepsy
  • Generalized Gaussian distribution
  • Leave-one-out cross-validation
  • Linear classifier
  • Wavelet filter banks

ASJC Scopus subject areas

  • Biomedical Engineering

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