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epileptic-seizure-prediction
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Application of Bhattacharyya distance as a dissimilarity index for automated prediction of epileptic seizures in rats
, Article 2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010, 15 June 2010 through 17 June 2010 ; 2010 ; 9781424466238 (ISBN) ; Vosoughi Vahdat, B ; Shamsollahi, M. B ; Sayyah, M ; Sharif University of Technology
Abstract
Seizures are defined as manifest of excessive and hypersynchronous activity of neurons in the cerebral cortex and represent a frequent malfunction of the human central nervous system. Therefore, the search for precursors and predictors of a seizure is of utmost clinical relevance and may even guide us to a deep understanding of the seizure generating mechanisms. In this study we analyzed invasive electroencephalogram (EEG) recordings in rats with experimentally induced generalized epilepsy with a nonlinear method called, dissimilarity index. In order to predict epileptic seizures automatically, Bhattacharyya distance between trajectory matrix of reference window, during an interval quite...
A new blind source separation approach based on dynamical similarity and its application on epileptic seizure prediction
, Article Signal Processing ; Volume 183 , 2021 ; 01651684 (ISSN) ; Nasrabadi, A. M ; Shamsollahi, M. B ; Sharif University of Technology
Elsevier B.V
2021
Abstract
Blind source separation is an important field of study in signal processing, in which the goal is to estimate source signals by having mixed observations. There are some conventional methods in this field that aim to estimate source signals by considering certain assumptions on sources. One of the most popular assumptions is the non-Gaussianity of sources which is the basis of many popular blind source separation methods. These methods may fail to estimate sources when the distribution of two or more sources is Gaussian. Hence, this study aims to introduce a new approach in blind source separation for nonlinear and chaotic signals by using a dynamical similarity measure and relaxing...
Analysis of Epileptic Rats' EEG and Detection and Prediction of Epileptic Seizures
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor) ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Epilepsy is one of the most significant neurological disorders that about one percent of people suffer from it. Epilepsy can only be controlled, and so far no cure for it has been provided. Despite the many advances in the treatment of diseases, for a quarter of patients there is no medical treatment solution for controlling epileptic seizures. In the studies of medical groups on the epilepsy, one approach is employment of some models for each type of epilepsy. These types may be created in the animals to allow studying of the mechanism of epilepsy and also finding drugs of treatment or controlling seizures for each type of epilepsy. There is a type of epilepsy that is called absence...
Defining an EEG Index for Seizure Prediction
, M.Sc. Thesis Sharif University of Technology ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
Epilepsy is one of the most common neurological disorders, second only to stroke, with a prevalence of 0.6–0.8% of the world’s population. Epilepsy is not cured, Two-thirds of the patients achieve sufficient seizure control from anticonvulsive medication, and another 8–10% could benefit from respective surgery. For the remaining 25% of patients, no sufficient treatment is currently available. In the recent decent, many studies in this field attempt to predict the onset time of a impending seizure by monitoring the biomedical signals, Electroencephalogram (EEG) Signal processing is one of these biomedical Signals that many In this work, First we talk about the basic concepts of Epilepsy ,...
Tracking dynamical transition of epileptic EEG using particle filter
, Article 8th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2008, Sarajevo, 16 December 2008 through 19 December 2008 ; February , 2008 , Pages 270-274 ; 9781424435555 (ISBN) ; Shamsollahi, M. B ; Hajipour, S ; IEEE Signal Processing Society and IEEE Computer Society ; Sharif University of Technology
2008
Abstract
In this work we used the Liley EEG model as a dynamical model of EEG. Two parameters of the model which are candidates for change during an epileptic seizure are defined as new states in state space representation of this dynamical model. Then SIS particle filter is applied for estimating the defined states over time using the recorded epileptic EEG as the observation of the system. A method for fast numerical solution of the nonlinear coupled equation of the model is proposed. This model is used for tracking the dynamical properties of brain during epileptic seizure. Tracking the changes of these new defined states of the model have good information about the state transition of the model...