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.

Real time epileptic seizure prediction using AR models and Support Vector Machines / L. Chisci; A. Mavino; G. Perferi; M. Sciandrone; C. Anile; G. Colicchio; F. Fuggetta. - In: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. - ISSN 0018-9294. - STAMPA. - 57(2010), pp. 1124-1132.

Real time epileptic seizure prediction using AR models and Support Vector Machines

CHISCI, LUIGI;SCIANDRONE, MARCO;
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.
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1124
1132
L. Chisci; A. Mavino; G. Perferi; M. Sciandrone; C. Anile; G. Colicchio; F. Fuggetta
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2158/388533
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