Search for: epileptic-seizure-detection
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    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
    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... 

    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... 

    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... 

    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  

    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)
    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... 

    Automatic epileptic seizure detection in a mixed generalized and focal seizure dataset

    , Article 26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019, 27 November 2019 through 28 November 2019 ; 2019 , Pages 172-176 ; 9781728156637 (ISBN) Mozafari, M ; Hajipour Sardouie, S ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient's seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that,... 

    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... 

    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)
    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... 

    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)
    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... 

    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
    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...