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Epileptic seizure prediction using relative spectral power features
TLDR
Proposed algorithm was evaluated on continuous long-term multichannel scalp and invasive recordings (183 seizures, 3565 h). Expand
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Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods
Combining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering fromExpand
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Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients
TLDR
The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. Expand
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Output regularization of SVM seizure predictors: Kalman Filter versus the “Firing Power” method
TLDR
Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. Expand
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Early Seizure Detection Using Neuronal Potential Similarity: A Generalized Low-Complexity and Robust Measure
TLDR
A novel approach using neuronal potential similarity (NPS) of two intracranial electroencephalogram electrodes placed over the foci is proposed for automated early seizure detection in patients with refractory partial epilepsy. Expand
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EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest
OBJECTIVE Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linearExpand
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On the proper selection of preictal period for seizure prediction
TLDR
We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. Expand
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Epileptic seizure prediction based on a bivariate spectral power methodology
TLDR
The spectral power of 5 frequently considered frequency bands (Alpha, Beta, Gamma, Theta and Delta) for 6 EEG channels is computed and then all the possible pairwise combinations among the 30 features set, are used to create a 435 dimensional feature space. Expand
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Nonlinear subspace clustering using curvature constrained distances
TLDR
We proposed a new algorithm for subspace clustering of data, where the data consists of several possibly intersected manifolds with the intersection. Expand
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Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features
Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimisticExpand
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