`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Computers in Biology and Medicine
One-Class Novelty Detection for Seizure Analysis from Intracranial EEG
The Journal of Machine Learning Research
Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection
Computers in Biology and Medicine
Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm
Computers in Biology and Medicine
Computers in Biology and Medicine
Differential operator in seizure detection
Computers in Biology and Medicine
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Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0%+/-10.0% and false positive rate was 0.20+/-0.22 per hour).