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.
2010
Autoregressive (AR) models; EEG signals; Epileptic seizure prediction; Kalman filtering; Support vector machines (SVMs); Computer Systems; Data Interpretation, Statistical; Diagnosis, Computer-Assisted; Electroencephalography; Epilepsy; Humans; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity; Algorithms; Artificial Intelligence
01 Pubblicazione su rivista::01a Articolo in rivista
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]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1625749
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