• ISSN 1674-8301
  • CN 32-1810/R
Volume 34 Issue 3
May  2020
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Quintero-Rincón Antonio, D'Giano Carlos, Batatia Hadj. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures[J]. The Journal of Biomedical Research, 2020, 34(3): 205-212. doi: 10.7555/JBR.33.20190012
Citation: Quintero-Rincón Antonio, D'Giano Carlos, Batatia Hadj. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures[J]. The Journal of Biomedical Research, 2020, 34(3): 205-212. doi: 10.7555/JBR.33.20190012

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

doi: 10.7555/JBR.33.20190012
More Information
  • Corresponding author: Antonio Quintero-Rincón, Epilepsy and Telemetry Integral Center, Fight Against Child Neurological Diseases Foundation, Montañeses 2325, Buenos Aires C1428AQK, Argentina. E-mail: tonioquintero@ieee.org
  • Received: 2019-01-16
  • Revised: 2019-04-12
  • Accepted: 2019-06-03
  • Published: 2019-08-28
  • Issue Date: 2020-05-28
  • 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.


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