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The present study proposes new method for epileptic seizure prediction based on heart rate variability (HRV) analysis and one-class support vector machines (SVM) technique. Methods: Excessive neural activity in preictal period affects not only brain activity, but also autonomic nervous system, that affects HRV. The proposed method distinguishes eight features, analyzes matrix of eigenvalues and eigenvectors,...
This work is devoted to the prediction of epileptic seizures using heart rate variability (HRV) characteristics. Several HRV features were extracted (statistical, spectral, histogram, polynomial approximation coefficients) for various durations of sliding time windows and various lengths of preictal intervals. The data from 14 subjects with generalized epileptic seizures was used. Support Vector Machine...
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