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In this paper, the equal frequency discretization (EFD) based probability density approach was proposed to be used in the diagnosis of epilepsy from electroencephalogram (EEG) signals. For this aim, EEG signals were decomposed by using the discrete wavelet discretization (DWT) method into subbands, the coefficients in each subband were discretized to several intervals by EFD method, and the probability...
Our objective is to analyze EEG signals recorded with depth electrodes during seizures in patients with drug-resistant epilepsy. Usually, different phases are observed during the seizure process, including a fast onset activity (FOA). We aim to determine how cerebral structures get involved during this FOA, in particular whether some structure can “drive” some other structures. This paper focuses...
This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction...
This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted...
This paper emphasizes on a VLSI design for SIRM fuzzy processor in biomedical application. A novel approach aims to identify and design a simple robust fuzzy system with minimum number of fuzzy rules to classify the epilepsy risk level of diabetic patients from cerebral blood flow and EEG signals is discussed in this paper. Four different types of fuzzy models are designed and tested with 200 patients...
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