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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) Niknazar, M ; 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) Niknazar, H ; 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 Functional Brain Connectivity Using EEG Signals for Classification of Brain States

    , M.Sc. Thesis Sharif University of Technology Ghodsi, Saeed (Author) ; Karbalai Aghajan, Hamid (Supervisor) ; Mohamadzadeh, Hoda ($item.subfieldsMap.e)
    Abstract
    Different perceptual, cognitive, and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brain response to the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective, and functional, s.t. the first one referes to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional connectivity between... 

    Investigation of Brain Connectivity Changes during Seizure using Graph Theory

    , M.Sc. Thesis Sharif University of Technology Khoshkhah Tinat, Atefeh (Author) ; Karbalai Aghajan, Hamid (Supervisor) ; Mohammadzadeh, Hoda (Co-Supervisor)
    Abstract
    Epilepsy is a chronic neurological disorder characterized by recurrent and abrupt seizures. Seizures occur due to disturbances in the interactions between the distributed neuronal populations in the brain. Investigation of the brain functional connectivity networks is a way to better understand how the brain functions during seizure. To estimate the brain functional connectivity network, we need criteria that can estimate the functional connections between the brain regions from the recorded brain functional data such as electroencephalogram (EEG) signals. After estimating the functional brain connectivity networks, it is possible to create graphs corresponding to these estimated networks... 

    A Novel Approach for Seizure Prediction using EEG Signals

    , M.Sc. Thesis Sharif University of Technology Shahbazi, Mohammad (Author) ; Karbalaei Aghajan, Hamid (Supervisor)
    Abstract
    As the fourth most common neurological disorder, epilepsy affects lots of people all around the world, some of whom have to live with unpredictable seizures uncontrollable by surgery or medication. Hence, Developing systems for detection and prediction of the epileptic seizures will help the patients to avoid the possible damages caused by sudden seizures. This study addresses the task of epileptic seizure prediction, using three different novel approaches. The first approach, which is based on anomaly detection, contains three steps: feature extraction from EEG signals, training a one-class SVM classifier, and a post-processing step. The second method exploits a recurrent neural network to... 

    A generalizable model for seizure prediction based on deep learning using CNN-LSTM architecture

    , Article 2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018, 26 November 2018 through 29 November 2018 ; 2019 , Pages 469-473 ; 9781728112954 (ISBN) Shahbazi, M ; Aghajan, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Abstract
    This work proposes a novel deep learning-based model for prediction of epileptic seizures using multichannel EEG signals. Multichannel images are first constructed by applying short-time Fourier transform (STFT) to Electroencephalography (EEG) signals. After a preprocessing step, a CNN-LSTM neural network is trained on the STFTs in order to capture the spectral, spatial and temporal features within and between the EEG segments and classify them as preictal or interictal stage. The proposed method achieves a sensitivity of 98.21%, a false prediction rate (FPR) of 0.13/h and a mean prediction time of 44.74 minutes on the CHB-MIT dataset. As the main contribution of this work, by using a... 

    Analysis of Epileptic Rats' EEG and Detection and Prediction of Epileptic Seizures

    , M.Sc. Thesis Sharif University of Technology Niknazar, Mohammad (Author) ; 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 Mamaghanian, Hossein (Author) ; 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) Mamaghanian, H ; 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...