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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
    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... 

    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,... 

    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 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... 

    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)
    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)
    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,... 

    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... 

    Psychogenic seizures and frontal disconnection: EEG synchronisation study

    , Article Journal of Neurology, Neurosurgery and Psychiatry ; Volume 82, Issue 5 , 2011 , Pages 505-511 ; 00223050 (ISSN) Knyazeva, M. G ; Jalili, M ; Frackowiak, R. S ; Rossetti, A. O ; Sharif University of Technology
    Objective Psychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients. Methods The authors analysed the whole-head... 

    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
    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... 

    Stockwell transform for epileptic seizure detection from EEG signals

    , Article Biomedical Signal Processing and Control ; Volume 38 , 2017 , Pages 108-118 ; 17468094 (ISSN) Kalbkhani, H ; Shayesteh, M. G ; Sharif University of Technology
    Epilepsy is the most common disorder of human brain. The goal of this paper is to present a new method for classification of epileptic phases based on the sub-bands of electroencephalogram (EEG) signals obtained from the Stockwell transform (ST). ST is a time-frequency analysis that not only covers the advantages of both short-time Fourier transform (FT) and wavelet transform (WT), but also overcomes their shortcomings. In the proposed method, at first, EEG signal is transformed into time-frequency domain using ST and all operations are performed in the new domain. Then, the amplitudes of ST in five sub-bands, namely delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), are computed. In... 

    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... 

    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... 

    Detection of rhythmic discharges in newborn EEG signals

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6577-6580 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mohseni, H. R ; Mirghasemi, H ; Shamsollahi, M. B ; Zamani, M. R ; Sharif University of Technology
    This paper presents a scalp electroencephalogram (EEG) rhythmic pattern detection scheme based on neural networks. Rhythmic discharges detection is applicable to the majority of seizures seen in newborns, and is listed as detecting 90% of all the seizures. In this approach some features based on various methods are extracted and compared by a modified multilayer neural network in order to find rhythmic discharges. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed. © 2006 IEEE  

    Seizure detection in EEG signals: a comparison of different approaches

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 6724-6727 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mohseni, H. R ; Maghsoudi, A ; Shamsollahi, M. B ; Sharif University of Technology
    In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database. © 2006 IEEE  

    Automatic detection of epileptic seizure using time-frequency distributions

    , Article IET 3rd International Conference MEDSIP 2006: Advances in Medical, Signal and Information Processing, Glasgow, 17 July 2006 through 19 July 2006 ; Issue 520 , 2006 , Pages 29- ; 0863416586 (ISBN); 9780863416583 (ISBN) Mohseni, H. R ; Maghsoudi, A ; Kadbi, M. H ; Hashemi, J ; Ashourvan, A ; Sharif University of Technology
    The aim of this work is to introduce a new method based on time frequency distribution for classifying the EEG signals. Some parameters are extracted using time-frequency distribution as inputs to a feed-forward backpropagation neural networks (FBNN). The proposed method had better results with 98.25% accuracy compared to previously studied methods such as wavelet transform, entropy, logistic regression and Lyapunov exponent  

    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
    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 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... 

    Denoising of ictal EEG data using semi-blind source separation methods based on time-frequency priors

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 19, Issue 3 , July , 2015 , Pages 839-847 ; 21682194 (ISSN) Hajipour Sardouie, S ; Shamsollahi, M. B ; Albera, L ; Merlet, I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2015
    Removing muscle activity from ictal ElectroEncephaloGram (EEG) data is an essential preprocessing step in diagnosis and study of epileptic disorders. Indeed, at the very beginning of seizures, ictal EEG has a low amplitude and its morphology in the time domain is quite similar to muscular activity. Contrary to the time domain, ictal signals have specific characteristics in the time-frequency domain. In this paper, we use the time-frequency signature of ictal discharges as a priori information on the sources of interest. To extract the time-frequency signature of ictal sources, we use the Canonical Correlation Analysis (CCA) method. Then, we propose two time-frequency based semi-blind source...