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Total 32 records

    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... 

    Role of Synchronous Sub-network in the Propagation of Synchronization to the Neuronal Population

    , M.Sc. Thesis Sharif University of Technology Naderi, Amir Mohammad (Author) ; Moghimi Araghi, Saman (Supervisor)
    Abstract
    Epilepsy is one of the most common non-communicable neurological disorders, characterized by recurrent seizure symptoms. Although much progress has been made in the diagnosis, control, and treatment of epilepsy in recent years, the exact mechanism of seizures, the specific method for early diagnosis of epilepsy and related syndromes, and definitive treatment for all patients are not yet known. In a type of seizure known as focal seizure, the electrical activity of neurons at the epilepsy focus synchronizes abnormally, and this synchronization can propagate to other regions of the brain in a process called secondary generalization, which finding a method for its prevention is our essential goal... 

    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 ,... 

    Seizure Detection in Generalized and Focal Seizure from EEG Signals

    , M.Sc. Thesis Sharif University of Technology Mozafari, Mohsen (Author) ; Hajipour, Sepideh (Supervisor)
    Abstract
    Epilepsy is one of the diseases that affects the quality of life of epileptic patients. Epileptic patients lose control during epileptic seizures and are more likely to face problems. Designing and creating a seizure detection system can reduce casualties from epileptic attacks. In this study, we present an automatic method that reduces the artifact from the raw signals, and then classifies the seizure and non-seizure epochs. At all stages, it is assumed that no information is available about the patient and this detection is made only based on the information of other patients. The data from this study were recorded in Temple Hospital and the recording conditions were not controlled, so... 

    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... 

    Epileptic Seizure Detetion by use of Accelerometer

    , M.Sc. Thesis Sharif University of Technology Ghaderi, Nasser (Author) ; Ahmadian, Mohammad-Taghi (Supervisor)
    Abstract
    After Alzheimer and brain attack, the most common neurological disorder is epilepsy, which often involves seizures. In two-thirds of patients with epilepsy, the seizures can be controlled by antiepileptic drugs, and about 8% of patients can use epilepsy surgery; but unfortunately there is no acceptable treatment for the other 25% of these patients. Therefore preventing from epilepsy losses is a very important topic.
    The gold standard for the diagnosis of the epilepsy is EEG monitoring. In this method, electrodes are placed on the scalp. Electrodes are uncomfortable to wear, and cause invasion to the patient, hence long-term monitoring and home monitoring is not feasible. In some... 

    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... 

    Dynamics of Magnetic Nanoparticles in Biologically Inspired Flows under Effect of Electric and Magnetic Fields with Application in Epilepsy Detection

    , Ph.D. Dissertation Sharif University of Technology Zamani Pedram, Maysam (Author) ; Alasty, Aria (Supervisor) ; Ghaffarzadeh, Ebrahim (Supervisor) ; Shamloo, Amir (Co-Advisor)
    Abstract
    This thesis presents my Ph.D. research study focusing on the dynamic analysis of magnetic nanoparticles (MNPs) for epilepsy and blood-brain barrier (BBB) applications. In this analysis, we took into account various parameters including the magnetic field, fluid behavior, geometry and material of MNPs. Based on this computational study, the generated magnetic field in epileptic foci results in the aggregation of nanoparticles. This may offer the advantage of using MNPs as a Magnetic Resonance Imaging (MRI) contrast agent. Furthermore, in this study, we also demonstrated and discussed the advantage of MNPs for crossing BBB using the external magnetic field. The outcome of this research project... 

    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... 

    Epileptic Signal Denoising Using Morphological Component Analysis Based on Dictionary Learning

    , M.Sc. Thesis Sharif University of Technology Ilmak Foroosh, Arman (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Abstract
    The prevalence of epilepsy in the world and the need for surgery to treat patients have made it essential to locate the site of epilepsy before surgery. One method is to apply source localization algorithms to the EEG signals of epileptic patients in the ictal and interictal periods. However, because these signals are contaminated with various noises, they are challenging to interpret and require noise cancellation. Therefore, various methods have been proposed to eliminate the noise. Among these methods, a new method recently used to remove noise from the epileptic signal is Morphological Component Analysis (MCA). This method uses the basic concepts of sparse representation of signals to... 

    Online analysis of local field potentials for seizure detection in freely moving rats

    , Article Iranian Journal of Basic Medical Sciences ; Volume 23, Issue 2 , 2020 , Pages 173-177 Zare, M ; Nazari, M ; Shojaei, A ; Raoufy, M. R ; Mirnajafi Zadeh, J ; Sharif University of Technology
    Mashhad University of Medical Sciences  2020
    Abstract
    Objective(s): Seizure detection during online recording of electrophysiological parameters is very important in epileptic patients. In the present study, online analysis of field potential recordings was used for detecting spontaneous seizures in epileptic animals. Materials and Methods: Epilepsy was induced in rats by pilocarpine injection. During the chronic period of the pilocarpine model, local field potential (LFP) recording was run for at least 24 hr. At the same time, video monitoring of the animals was done to determine the real time of seizure occurrence. Both power and sample entropy of LFP were used for online analysis. Results: Obtained results showed that changes in LFP power... 

    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... 

    Epileptic seizure detection using neural fuzzy networks

    , Article 2006 IEEE International Conference on Fuzzy Systems, Vancouver, BC, 16 July 2006 through 21 July 2006 ; 2006 , Pages 596-600 ; 10987584 (ISSN); 0780394887 (ISBN); 9780780394889 (ISBN) Sadati, N ; Mohseni, H. R ; Maghsoudi, A ; Sharif University of Technology
    2006
    Abstract
    The electroencephalogram (EEG) is a representative signal containing information about the condition of the brain. The shape of the wave may contain useful information about its state. However, the human observer cannot directly monitor these subtle details. Besides, since bio-signals are highly subjective, the symptoms may appear at random in the time scale. Therefore, the EEG signal parameters, extracted and analyzed using computers, are highly useful in diagnosis. The aim of this work is to compare the different classifiers when applied to EEG data from normal and epileptic subjects. For this purpose an adaptive neural fuzzy network (ANFN) to classify normal and epileptic EEG signals is... 

    A new framework based on recurrence quantification analysis for epileptic seizure detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
    2013
    Abstract
    This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

    A unified approach for detection of induced epileptic seizures in rats using ECoG signals

    , Article Epilepsy and Behavior ; Volume 27, Issue 2 , 2013 , Pages 355-364 ; 15255050 (ISSN) Niknazar, M ; Mousavi, S. R ; Motaghi, S ; Dehghani, A ; Vosoughi Vahdat, B ; Shamsollahi, M. B ; Sayyah, M ; Noorbakhsh, S. M ; Sharif University of Technology
    2013
    Abstract
    Objective: Epileptic seizure detection is a key step for epilepsy assessment. In this work, using the pentylenetetrazole (PTZ) model, seizures were induced in rats, and ECoG signals in interictal, preictal, ictal, and postictal periods were recorded. The recorded ECoG signals were then analyzed to detect epileptic seizures in the epileptic rats. Methods: Two different approaches were considered in this work: thresholding and classification. In the thresholding approach, a feature is calculated in consecutive windows, and the resulted index is tracked over time and compared with a threshold. The moment the index crosses the threshold is considered as the moment of seizure onset. In the... 

    Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

    , Article IRBM ; Volume 33, Issue 5-6 , December , 2012 , Pages 298-307 ; 19590318 (ISSN) Niknazar, M ; Mousavi, S. R ; Shamsollahi, M. B ; Vosoughi Vahdat, B ; Sayyah, M ; Motaghi, S ; Dehghani, A ; Noorbakhsh, S. M ; Sharif University of Technology
    2012
    Abstract
    Objective: Epileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats. Methods: Seizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal... 

    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 dissimilarity index of EEG signals for epileptic seizure detection

    , Article Final Program and Abstract Book - 4th International Symposium on Communications, Control, and Signal Processing, ISCCSP 2010, 3 March 2010 through 5 March 2010 ; March , 2010 ; 9781424462858 (ISBN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Shamsollahi, M. B ; Sayyah, M ; Sharif University of Technology
    2010
    Abstract
    Epileptic seizures are generated by an abnormal synchronization of neurons. Since epileptic seizures are unforeseeable for the patients, epileptic seizures detection is an interesting issue in epileptology, that novel approaches to understand the mechanism of epileptic seizures. In this study we analyzed invasive electroencephalogram (EEG) recordings in patients suffering from medically intractable focal epilepsy with a nonlinear method called, dissimilarity index. In order to detect epileptic seizures Bhattacharyya distance between trajectory matrix of reference window during an interval quite distant in time from any seizure and trajectory matrix of present window is employed to measure... 

    Performance analysis of EEG seizure detection features

    , Article Epilepsy Research ; Volume 167 , 2020 Niknazar, H ; Mousavi, S. R ; Niknazar, M ; Mardanlou, V ; Coelho, B. N ; Sharif University of Technology
    Elsevier B.V  2020
    Abstract
    Automatic detection of epileptic seizures can serve as a valuable clinical tool which involves a more objective and computationally efficient method for the analysis of EEG data in order to generate increasingly accurate and reliable results. Automatic seizure detection is also an important component of closed-loop responsive cortical stimulation systems. The goal of this study is to evaluate EEG-based features recently proposed for seizure detection to discover the optimum ones for a reliable seizure detection system. We extracted seizure detection features from intracranial EEG signals that were recorded during invasive pre-surgical epilepsy monitoring of people with drug resistant focal... 

    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...