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.

Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., Fuggetta, F., Real-time epileptic seizure prediction using AR models and support vector machines., <<IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING>>, 2010; 2010 (Maggio): 1124-1132 [http://hdl.handle.net/10807/33879]

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

Anile, Carmelo;Colicchio, Gabriella;
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.
Inglese
Chisci, L., Mavino, A., Perferi, G., Sciandrone, M., Anile, C., Colicchio, G., Fuggetta, F., Real-time epileptic seizure prediction using AR models and support vector machines., <<IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING>>, 2010; 2010 (Maggio): 1124-1132 [http://hdl.handle.net/10807/33879]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/33879
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