A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures

Antonio Quintero-Rincon*, Carlos D'Giano, Hadj Batatia

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)
22 Downloads (Pure)


The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals. The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique. The model fitting was assessed using four statistical parameters, which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events. The proposed method was applied to 66 epochs from the Children Hospital Boston database. Results showed that the method achieved fast and accurate detection of epileptic seizures, with a 92% sensitivity, 96% specificity, and 94.1% accuracy.

Original languageEnglish
Pages (from-to)205-212
Number of pages8
JournalJournal of Biomedical Research
Issue number3
Early online date28 Aug 2019
Publication statusPublished - May 2020


  • Curve fitting
  • Electroencephalogram
  • Epilepsy
  • Parabolic curves
  • Random forest
  • Two-point central difference

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)


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