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Epilepsy is one of the most common and diverse set of chronic neurological disorders characterized by an abnormal excessive or synchronous neuronal activity in the brain that is termed “seizure”, affecting about 50 million individuals worldwide. Electroencephalogram (EEG) signal processing technique plays a significant role in detection and prediction of epileptic seizure. Recently, many research...
In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on expert's knowledge and reasoning. A spatial-temporal...
Abstract-This paper presents a new approach to recognize and predict succedent epileptic seizures by using single-channel electroencephalogram (EEG) analysis. Eight channels of EEG from each patient of the seven consenting patients with generalized epilepsy were collected in Epilepsy Center of Xijing Hospital. The raw EEGs were decomposed by the algorithm of empirical mode decomposition (EMD), the...
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,...
In this paper, we attempt to analyze the effectiveness of the Empirical Mode Decomposition (EMD) for discriminating epilepticl periods from the interictal periods. The Empirical Mode Decomposition (EMD) is a general signal processing method for analyzing nonlinear and nonstationary time series. The main idea of EMD is to decompose a time series into a finite and often small number of intrinsic mode...
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