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.
In this paper, a square-root equality-constrained linear filter is used to track a target moving along a road. This choice provides a computationally efficient, algorithmically simple and numerically robust solution for the implementation of a VS-DMM (Variable Structure -Dynamic Multiple Model) filter. A performance evaluation via Monte Carlo simulations shows the effectiveness of the proposed approach.
II. GENERALIZED INFORMATION ARRAYSHereafter the notation v f"V (m, Q) will be used to denote that v is a random vector of mean m and covariance Q. For the purpose of linear estimation, all the useful information on a random vector x can be represented by the following linear model:
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