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. © 2006 IEEE.
Real-time epileptic seizure prediction using AR models and support vector machines / Chisci, L.; Mavino, A.; Perferi, G.; Sciandrone, M.; Anile, C.; Colicchio, G.; Fuggetta, F.. - In: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. - ISSN 0018-9294. - 57:5(2010), pp. 1124-1132. [10.1109/TBME.2009.2038990]
Real-time epileptic seizure prediction using AR models and support vector machines
Sciandrone M.;Colicchio G.;
2010
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. © 2006 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.