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IEEE Trans Biomed Eng. 2010 May;57(5):1124-32. doi: 10.1109/TBME.2009.2038990. Epub 2010 Feb 17.

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

Author information

  • 1Department of Systems and Informatics, University of Florence, Florence 50139, Italy. chisci@dsi.unifi.it

Abstract

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.

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