Real-time epileptic seizure prediction using AR models and support vector machines.

Luigi Chisci, Antonio Mavino, Guido Perferi, Marco Sciandrone, Carmelo Anile, Gabriella Colicchio, Filomena Fuggetta

Risultato della ricerca: Contributo in rivistaArticolo in rivista

170 Citazioni (Scopus)


This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.
Lingua originaleEnglish
pagine (da-a)1124-1132
Numero di pagine9
RivistaIEEE Transactions on Biomedical Engineering
Stato di pubblicazionePubblicato - 2010


  • Epileptic seizure
  • prediction


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