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EnglishThis 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]
| Autori: | |
| Titolo: | Real-time epileptic seizure prediction using AR models and support vector machines. |
| Data di pubblicazione: | 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. |
| Lingua: | Inglese |
| Rivista: | |
| Citazione: | 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] |
| Appare nelle tipologie: | Articolo in rivista, Nota a sentenza |
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