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    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... 

    Fetal electrocardiogram R-peak detection using robust tensor decomposition and extended Kalman filtering

    , Article Computing in Cardiology ; Volume 40 , 2013 , Pages 189-192 ; 23258861 (ISSN) ; 9781479908844 (ISBN) Akhbari, M ; Niknazar, M ; Jutten, C ; Shamsollahi, M. B ; Rivet, B ; Sharif University of Technology
    In this paper, we propose an efficient method for R-peak detection in noninvasive fetal electrocardiogram (ECG) signals which are acquired from multiple electrodes on mother's abdomen. The proposed method is performed in two steps: first, we employ a robust tensor decomposition-based method for fetal ECG extraction, assuming different heart rates for mother and fetal ECG; then a method based on extended Kalman filter (EKF) in which the ECG beat is modeled by 3 state equations (P, QRS and T), is used for fetal R-peak detection. The results show that the proposed method is efficiently able to estimate the location of R-peaks of fetal ECG signals. The obtained average scores of event 4 and 5 on... 

    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... 

    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,...