A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue The Journal of Biomedical Research Année : 2020

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

Résumé

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.
Fichier principal
Vignette du fichier
QuinteroRincon_26050.pdf (624.31 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-02875045 , version 1 (19-06-2020)

Identifiants

Citer

Antonio Quintero Rincón, Carlos d'Giano, Hadj Batatia. A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures. The Journal of Biomedical Research, 2020, 34 (3), pp.203-210. ⟨10.7555/JBR.33.20190012⟩. ⟨hal-02875045⟩
50 Consultations
50 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More