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    A new epileptic EEG spike detection based on mathematical morphology

    , Article Proceedings of the IASTED International Conference on Biomedical Engineering, Innsbruck, 16 February 2004 through 18 February 2004 ; 2004 , Pages 301-305 ; 0889863792 (ISBN); 9780889863798 (ISBN) Sarang, R ; Shamsollahi, M. B ; Khalilzadeh, M. A ; Senhadji, L ; Sharif University of Technology
    The best usual way in order to diagnosis, control and therapy different kind of Epilepsy is to refer to patient's EEG signals. Usually, in normal conditions of a patient, Epilepsy shows itself through EEG signals in the shape of transient waves. Identification of this waves using human eyes is so difficult that automatic detection methods based on mathematics and signal processing, should have been applied as an auxiliary tool to help physician identify this waves. The most significant transient epileptic waves in EEG are spikes. Up to now, various methods are presented for detection of spikes, but a quantitative and comparative evaluation hasn't been performed. In this work, we suggested a... 

    Quantitative Analysis of Epileptic Seizure EEG

    , M.Sc. Thesis Sharif University of Technology Hoseini, Mahmood (Author) ; Rahimi Tabar, Mohammad Reza (Supervisor)
    Since recording first electroencephalogram (EEG) of human brain in 1929 until now it becomes as a powerful tool in neuroscience. At first information extraction was done by visionary approaches only. But because of some problems in the context of inaccuracy and also in analyzing data different methods were proposed in order to extract hidden information of EEG. Among these approaches Fourier transformation was suggested as a very useful method that could draw out so many characteristics of signal different frequency components. However this way had many faults that cause limitation in analyzing time series and as a result other methods have been considered. One method that later has been... 

    Superparamagnetic nanoparticles for epilepsy detection

    , Article World Congress on Medical Physics and Biomedical Engineering, 2015, 7 June 2015 through 12 June 2015 ; Volume 51 , June , 2015 , Pages 1237-1240 ; 16800737 (ISSN) ; 9783319193878 (ISBN) Pedram, M. Z ; Shamloo, A ; Alasty, A ; Ghafar Zadeh, E ; Jaffray D. A ; Sharif University of Technology
    Springer Verlag  2015
    Epilepsy is the most common neurological disorder that is known with uncontrolled seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. The precise detection of an epileptic zone is key to its treatment. In this paper, we propose a method of epilepsy detection using brain magnetic field. The possibility of superparamagnetic nanoparticle (SPMN) as sensors for the detection of the epileptic area inside the brain is investigated. The aggregation of nanoparticles in the weak magnetic field of epileptic brain is modeled using potential... 

    Epileptic seizure detection using AR model on EEG signals

    , Article 2008 Cairo International Biomedical Engineering Conference, CIBEC 2008, Cairo, 18 December 2008 through 20 December 2008 ; February , 2008 ; 9781424426959 (ISBN) Mousavi, R ; Niknazar, M ; Vosughi Vahdat, B ; Sharif University of Technology
    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 based on it. These parameters are used as a feature to classify the EEG signals into Healthy, Interictal (seizure free) and Ictal (during a seizure) groups using multilayer perceptron (MLP) classifier. Correct classification scores at the range of 91% to 96% reveals the potential of our approach... 

    EEG Denoising Using Combination of Kalman Filtetring and Blind Source Separation Approaches for Epileptic Components Extraction

    , M.Sc. Thesis Sharif University of Technology Mohammadi, Marzieh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    Epilepsy is a neurological disorder whose prevalence is estimated to be 1% of the world population. Electroencephalogram (EEG) is one of the best and convenient non-invasive tools used in diagnosis and analysis of this disease. Epileptic components extracted from EEG recordings are widely used in neuroscience in the diagnosis analysis like epilepsy source localization. However, epileptic components are often contaminated and covered with artifacts of physiological origin (baseline, EMG, ECG, EOG, etc.) or instrument noises (power supply, electrode, etc.). So, preprocessing and denoising is necessary for precise analysis of epilepsy EEG recording. Heretofore, several methods have been... 

    Computer Aided Prognosis of Epileptic Patients Using Multi-Modality Data and Artificial Intelligence Techniques

    , M.Sc. Thesis Sharif University of Technology Latifi-Navid, Masoud (Author) ; Soltanian-Zadeh, Hamid (Supervisor)
    Abnormality detection and prognosis of epileptic patients with artificial intelligence and machine learning techniques is still in its early experimental stages. Surgical candidacy determination for epilepsy depends on the clinical actions which involve an intracranial electrode implantation followed by prolonged electrographic monitoring (EEG phase II) .This invasive test is very costly, painful and time consuming. Here the goal is integration of the two following paradigms: 1-Non invasive multimodality data of epilepsy. 2- Artificial intelligence and machine learning techniques. We have used human brain multi-modality database system that includes patient’s demographics, clinical and EEG... 

    Signal Subspace Identification for Epileptic Source Localization from EEG Data

    , Ph.D. Dissertation Sharif University of Technology Hajipour Sardouie, Sepideh (Author) ; Shamsollahi, Mohammad Bagher (Supervisor) ; Albera, Laurent (Co-Advisor) ; Merlet, Isabelle (Co-Advisor)
    In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing... 

    Epileptic Seizure Detetion by use of Accelerometer

    , M.Sc. Thesis Sharif University of Technology Ghaderi, Nasser (Author) ; Ahmadian, Mohammad-Taghi (Supervisor)
    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... 

    Alterations of the electroencephalogram sub-bands amplitude during focal seizures in the pilocarpine model of epilepsy

    , Article Physiology and Pharmacology ; Volume 16, Issue 1 , 2012 , Pages 11-20 ; 17350581 (ISSN) Motaghi, S ; Niknazar, M ; Sayyah, M ; babapour, V ; Vahdat, B. V ; Shamsollahi, M. B ; Sharif University of Technology
    Introduction: Temporal lobe epilepsy (TLE) is the most common and drug resistant epilepsy in adults. Due to behavioral, morphologic and electrographic similarities, pilocarpine model of epilepsy best resembles TLE. This study was aimed at determination of the changes in electroencephalogram (EEG) sub-bands amplitude during focal seizures in the pilocarpine model of epilepsy. Analysis of these changes might help detection of a pre-seizure state before an oncoming seizure. Methods: Rats were treated by scopolamine (1mg/kg, s.c) to prevent cholinergic effects. After 30 min, pilocarpine (380 mg/kg, i.p) was administered to induce status epilepticus (SE) and 2 hours after SE, diazepam (20 mg/kg,... 

    Recognizing Center of Siezur with Clustering Algorithm

    , M.Sc. Thesis Sharif University of Technology Akhshi, Amin (Author) ; Rahimitabar, Mohammad Reza (Supervisor)
    Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporall structures. Examples of complex systems are turbulent flow, stock markets, dynamics of a brain, etc. In study of the complex systems, we always encounter with handling and analysing of a Big-Data set. There are several approaches to overcome this problem, among which the most powerful method is the clustering analysis. Clustering algorithm is based on the classifying of dynamics of complex system using some similarity... 

    Using Bump Modeling in Brain Wave Analysis

    , M.Sc. Thesis Sharif University of Technology Ghanbari Garakani, Zahra (Author) ; Shamsollahi, Mohammad Bagher (Supervisor)
    In this thesis, the efficiency of bump modeling has been investigated on brain signals, in a variety of aspects including analysis, detection, classification and prediction. The aim of bump modeling is to provide an optimized representation of the signal in time-frequency domain. This would be done by discriminating oscillatory bursts from background signal and then showing them by half-ellipsoid functions called bump. Consequently, the problem of dealing with large numbers of parameters and hence complicated calculations, which are serious concerns in similar methods, can be overcome. This is in addition to the benefits of using time-frequency representation of the signal.The aim of bump... 

    Seizure Detection in Generalized and Focal Seizure from EEG Signals

    , M.Sc. Thesis Sharif University of Technology Mozafari, Mohsen (Author) ; Hajipour, Sepideh (Supervisor)
    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... 

    Reconstruction of Jump-diffusion Model from Epileptic Brain Signal and Pyramidal Neurons Potential in an Electric Fish

    , M.Sc. Thesis Sharif University of Technology Shafaee, Yasaman (Author) ; Rahimi Tabar, Mohammad Reza (Supervisor)
    Complex systems involve a large number of degrees of freedom and consist of many components. Interactions of these components with each other, or with an external force, play a significant role in the collective behavior of the complex system.We come across complex systems in many different fields of study including neuroscience, climatology, studying stock markets, etc. The non-linearity of the interactions between their components is what they have in common. Interesting macro-scale properties can be observed in a complex system, as a result of the collective behavior of the system components. We usually focus on studying a group of components in a system, rather than a single component,... 

    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)
    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... 

    Complex Dynamics of Epileptic Brain and Turbulence :From Time Series to Information Flow

    , Ph.D. Dissertation Sharif University of Technology Anvari, Mehrnaz (Author) ; Rahimi Tabar, Mohammad Reza (Supervisor) ; Karimipour, Vahid (Supervisor)
    Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporal and functional structures. The dynamics of order parameters in complex systems are generally non-stationary and can interact with each other in nonlinear manner. As a result, the analysis of the behavior of complex systems must be based on the assessment of the nonlinear interactions, as well as the determination of the characteristics and the strength of the fluctuating forces. This leads to the problem of retrieving a... 

    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
    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... 

    Epileptic seizure detection based on video and EEG recordings

    , Article 2017 IEEE Biomedical Circuits and Systems Conference, BioCAS 2017 - Proceedings, 19 October 2017 ; Volume 2018-January , 2018 , Pages 1-4 ; 9781509058037 (ISBN) Aghaei, H ; Kiani, M. M ; Aghajan, H ; IEEE Circuits and Systems Society (CAS); IEEE Engineering in Medicine and Biology Society (EMBS); SSCS ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2018
    Clinical data from epileptic patients reveal important information about the characteristics of the particular type of epilepsy. Such data is often acquired in a bimodal fashion, e.g. video recordings are collected with the standard Electroencephalogram (EEG) data, in order to help the specialists validate their assessment based on one modality with the other. Manual annotation of the onset of seizures across several days' worth of data is time consuming. This paper proposes an automated epilepsy seizure detection method based on a combination of features from EEG and video data, and compares it against detectors using either modality alone. © 2017 IEEE  

    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
    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... 

    Hippocampal shape analysis in the Laplace Beltrami feature space for temporal lobe epilepsy diagnosis and lateralization

    , Article Proceedings - International Symposium on Biomedical Imaging ; 2012 , Pages 150-153 ; 19457928 (ISSN) ; 9781457718588 (ISBN) Shishegar, R ; Gandomkar, Z ; Soltaman Zadeh, H ; Moghadasi, S. R ; Sharif University of Technology
    IEEE  2012
    Shape analysis plays an important role in many medical imaging studies. One of the recent shape analysis methods uses the Laplace Beltrami operator which is also used in this paper for hippocampal shape comparison. We proposed a feature vector which consists of size measures and shape descriptors based on Laplace Beltrami eigenvalues and eigenfunctions. The aforementioned feature space is utilised for automatic differentiating normal subjects from epileptic patients as well as distinguishing epileptic patients with left temporal lobe epilepsy (LTLE) from patients with right temporal lobe epilepsy (RTLE). Achieved results are diagnostic accuracy of 93% with 95% sensitivity and lateralization... 

    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
    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...