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This study evaluated the capability of neural classifier to perform the separation between epileptiform and non-epileptiform events. To processing the EEG signals was used the Wavelet Transform through the use of the Coiflet1 function. The main elements present in the EEG signals were separated in five distinct event classes (spikes, sharp waves, blinks, background activity and noise). All the events...
This paper presents a novel theoretical paradigm for epileptic seizure prediction based on a coupled oscillator model of brain dynamics. This model is used to investigate prediction methods capable of tracking the synchronization changes that may lead to a seizure. Previous results indicate that state-space reconstruction of a coupled oscillator model from an EEG-like signal is ill-posed, therefore,...
Time-frequency analysis is a robust signal processing tool for the estimation of time-frequency relation and is widely used to analyze electroencephalographic (EEG). EEG recordings provide valuable information about electrical activity of the neurons in the brain, so it is an important tool in studying and recognizing several neurological disorders like epilepsy. The main goal of this paper was to...
Coherence is a widely used measure for characterizing linear dependence between a pair of signals. For nonstationary signals, the autospectrum, cross spectrum, and coherence between signals may evolve over time. A standard approach is to divide the signals into overlapping blocks of fixed width and then smooth (over frequency) the periodogram matrix at each time block. In this paper, a consistent...
In this paper, several manifold learning (ML) techniques for dimension reduction of EEG feature vectors are introduced and applied on set of epileptic EEG signals. These include principal component analysis (PCA), multidimensional scaling (MDS), isometric mapping (ISOMAP) and locally linear embedding (LLE). While EEG signals of epileptic patients contain necessary information with regards to the various...
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