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    Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data

    , Article Cognitive Neurodynamics ; Volume 14, Issue 4 , 2020 , Pages 457-471 Ghahari, S ; Farahani, N ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer  2020
    Abstract
    Investigating human brain activity during expressing emotional states provides deep insight into complex cognitive functions and neurological correlations inside the brain. To be able to resemble the brain function in the best manner, a complex and natural stimulus should be applied as well, the method used for data analysis should have fewer assumptions, simplifications, and parameter adjustment. In this study, we examined a functional magnetic resonance imaging dataset obtained during an emotional audio-movie stimulus associated with human life. We used Jackknife Correlation (JC) method to derive a representation of time-varying functional connectivity. We applied different binary measures... 

    Stockwell transform of time-series of fMRI data for diagnoses of attention deficit hyperactive disorder

    , Article Applied Soft Computing Journal ; Volume 86 , 2020 Sartipi, S ; Kalbkhani, H ; Ghasemzadeh, P ; Shayesteh, M. G ; Sharif University of Technology
    Elsevier Ltd  2020
    Abstract
    Attention deficit hyperactivity disorder (ADHD) is a common brain disorder among children. It presents various symptoms, hence, utilizing the information obtained from functional magnetic resonance imaging (fMRI) time-series data can be useful. Finding functional connections in typically developed control (TDC) and ADHD patients can be helpful in classification. The aim of this paper is to present a multifold method for the study of fMRI data to diagnose ADHD patients. In the proposed method, first, by applying the Stockwell transform (ST), we obtain detailed information about the time-series of the region of interests (ROIs) in the time and frequency domains. ST provides information about... 

    An artificial multi-channel model for generating abnormal electrocardiographic rhythms

    , Article Computers in Cardiology 2008, CAR, Bologna, 14 September 2008 through 17 September 2008 ; Volume 35 , 2008 , Pages 773-776 ; 02766574 (ISSN); 1424437067 (ISBN); 9781424437061 (ISBN) Clifford, G. D ; Nemati, S ; Sameni, R ; Sharif University of Technology
    2008
    Abstract
    We present generalizations of our previously published artificial models for generating multi-channel ECG so that the simulation of abnormal rhythms is possible. Using a three-dimensional vectorcardiogram (VCG) formulation, we generate the normal cardiac dipole for a patient using a sum of Gaussian kernels, fitted to real VCG recordings. Abnormal beats are then specified either as new dipoles, or as perturbations of the existing dipole. Switching between normal and abnormal beat types is achieved using a hidden Markov model (HMM). Probability transitions can be learned from real data or modeled by coupling to heart rate and sympathovagal balance. Natural morphology changes form beat-to-beat... 

    Classification of sleep stages based on LSTAR model

    , Article Applied Soft Computing Journal ; Volume 75 , 2019 , Pages 523-536 ; 15684946 (ISSN) Ghasemzadeh, P ; Kalbkhani, H ; Sartipi, S ; Shayesteh, M. G ; Sharif University of Technology
    Elsevier Ltd  2019
    Abstract
    Sleep study is very important in the health since sleep disorders affect the productivity of individuals. One of the important topics in sleep research is the classification of sleep stages using the electroencephalogram (EEG) signal. Electrical activities of brain are measured by EEG signal in the laboratory. In real-world environments, EEG signal is also used in portable monitoring devices to analyze sleep. In this study, we propose an efficient method for classification of sleep stages. EEG signals are examined by a new model from autoregressive (AR) family, namely logistic smooth transition autoregressive (LSTAR) to study sleep process. In contrast to the AR model, LSTAR is a non-linear... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    fMRI functional connectivity analysis via kernel graph in Alzheimer’s disease

    , Article Signal, Image and Video Processing ; Volume 15, Issue 4 , 2021 , Pages 715-723 ; 18631703 (ISSN) Ahmadi, H ; Fatemizadeh, E ; Motie-Nasrabadi, A ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Abstract
    Functional magnetic resonance imaging (fMRI) is an imaging tool that is used to analyze the brain’s functions. Brain functional connectivity analysis based on fMRI signals often calculated correlations among time series in different areas of the brain. For FC analysis most prior research works generate the brain graphs based on linear correlations, however, the nonlinear behavior of the brain can lower the accuracy of such graphs. Usually, the Pearson correlation coefficient is used which has limitations in revealing nonlinear relationships. One of the proper methods for nonlinear analysis is the Kernel trick. This method maps the data into a high dimensional space and calculates the linear... 

    Deep long short-term memory (LSTM) networks for ultrasonic-based distributed damage assessment in concrete

    , Article Cement and Concrete Research ; Volume 162 , 2022 ; 00088846 (ISSN) Ranjbar, I ; Toufigh, V ; Sharif University of Technology
    Elsevier Ltd  2022
    Abstract
    This paper presented a comprehensive study on developing a deep learning approach for ultrasonic-based distributed damage assessment in concrete. In particular, two architectures of long short-term memory (LSTM) networks were proposed: (1) a classification model to evaluate the concrete's damage stage; (2) a regression model to predict the concrete's absorbed energy ratio. Two input configurations were considered and compared for both architectures: (1) the input was a single signal; (2) the inputs were four signals from four sides of the specimen. A comprehensive experimental study was designed and conducted on ground granulated blast furnace slag-based geopolymer concrete, providing a... 

    Properties of functional brain networks correlate frequency of psychogenic non-epileptic seizures

    , Article Frontiers in Human Neuroscience ; Issue DEC , 2012 ; 16625161 (ISSN) Barzegaran, E ; Joudaki, A ; Jalili, M ; Rossetti, A. O ; Frackowiak, R. S ; Knyazeva, M. G ; Sharif University of Technology
    Frontiers Media S. A  2012
    Abstract
    Abnormalities in the topology of brain networks may be an important feature and etiological factor for psychogenic non-epileptic seizures (PNES). To explore this possibility, we applied a graph theoretical approach to functional networks based on resting state EEGs from 13 PNES patients and 13 age- and gender-matched controls. The networks were extracted from Laplacian-transformed time-series by a cross-correlation method. PNES patients showed close to normal local and global connectivity and small-world structure, estimated with clustering coefficient, modularity, global efficiency, and small-worldness metrics, respectively. Yet the number of PNES attacks per month correlated with a... 

    EEG-based functional networks in schizophrenia

    , Article Computers in Biology and Medicine ; Volume 41, Issue 12 , 2011 , Pages 1178-1186 ; 00104825 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    2011
    Abstract
    Schizophrenia is often considered as a dysconnection syndrome in which, abnormal interactions between large-scale functional brain networks result in cognitive and perceptual deficits. In this article we apply the graph theoretic measures to brain functional networks based on the resting EEGs of fourteen schizophrenic patients in comparison with those of fourteen matched control subjects. The networks were extracted from common-average-referenced EEG time-series through partial and unpartial cross-correlation methods. Unpartial correlation detects functional connectivity based on direct and/or indirect links, while partial correlation allows one to ignore indirect links. We quantified the... 

    Evaluating the toxic effect of an antimicrobial agent on single bacterial cells with optical tweezers

    , Article Biomedical Optics Express ; Volume 6, Issue 1 , 2015 , Pages 112-117 ; 21567085 (ISSN) Samadi, A ; Zhang, C ; Chen, J ; Reihani, S. N. S ; Chen, Z ; Sharif University of Technology
    OSA - The Optical Society  2015
    Abstract
    We implement an optical tweezers technique to assess the effects of chemical agents on single bacterial cells. As a proof of principle, the viability of a trapped Escherichia coli bacterium is determined by monitoring its flagellar motility in the presence of varying concentrations of ethyl alcohol. We show that the “killing time” of the bacterium can be effectively identified from the correlation statistics of the positional time series recorded from the trap, while direct quantification from the time series or associated power spectra is intractable. Our results, which minimize the lethal effects of bacterial photodamage, are consistent with previous reports of ethanol toxicity that used... 

    Autoregressive video modeling through 2D Wavelet Statistics

    , Article Proceedings - 2010 6th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP 2010, 15 October 2010 through 17 October 2010 ; October , 2010 , Pages 272-275 ; 9780769542225 (ISBN) Omidyeganeh, M ; Ghaemmaghami, S ; Shirmohammadi, S ; Sharif University of Technology
    2010
    Abstract
    We present an Autoregressive (AR) modeling method for video signal analysis based on 2D Wavelet Statistics. The video signal is assumed to be a combination of spatial feature time series that are temporally approximated by the AR model. The AR model yields a linear approximation to the temporal evolution of a stationary stochastic process. Generalized Gaussian Density (GGD) parameters, extracted from 2D wavelet transform subbands, are used as the spatial features. Wavelet transform efficiently resembles the Human Visual System (HVS) characteristics and captures more suitable features, as compared to color histogram features. The AR model describes each spatial feature vector as a linear... 

    Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting

    , Article Knowledge-Based Systems ; Volume 23, Issue 8 , 2010 , Pages 800-808 ; 09507051 (ISSN) Hadavandi, E ; Shavandi, H ; Ghanbari, A ; Sharif University of Technology
    Abstract
    Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN) for constructing a stock price forecasting expert system. At first, we use stepwise regression analysis (SRA) to determine factors which have most influence on stock prices. At the next stage we divide our raw data into k clusters by means of self-organizing map (SOM) neural networks. Finally, all... 

    Conceptualization of karstic aquifer with multiple outlets using a dual porosity model

    , Article Groundwater ; Volume 55, Issue 4 , 2017 , Pages 558-564 ; 0017467X (ISSN) Hosseini, S. M ; Ataie Ashtiani, B ; Sharif University of Technology
    Abstract
    In this study, two conceptual models, the classic reservoir (CR) model and exchange reservoirs model embedded by dual porosity approach (DPR) are developed for simulation of karst aquifer functioning drained by multiple outlets. The performances of two developed models are demonstrated at a less developed karstic aquifer with three spring outlets located in Zagros Mountain in the south-west of Iran using 22-years of daily data. During the surface recharge, a production function based on water mass balance is implemented for computing the time series of surface recharge to the karst formations. The efficiency of both models has been assessed for simulation of daily spring discharge during the... 

    Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series

    , Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 Ahmadi, H ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
    IOP Publishing Ltd  2020
    Abstract
    Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three... 

    Iran atlas of offshore renewable energies

    , Article Renewable Energy ; Volume 36, Issue 1 , January , 2011 , Pages 388-398 ; 09601481 (ISSN) Abbaspour, M ; Rahimi, R ; Sharif University of Technology
    2011
    Abstract
    The aim of the present study is to provide an Atlas of IRAN Offshore Renewable Energy Resources (hereafter called 'the Atlas') to map out wave and tidal resources at a national scale, extending over the area of the Persian Gulf and Sea of Oman. Such an Atlas can provide necessary tools to identify the areas with greatest resource potential and within reach of present technology development. To estimate available tidal energy resources at the site, a two-dimensional tidally driven hydrodynamic numerical model of Persian Gulf was developed using the hydrodynamic model in the MIKE 21 Flow Model (MIKE 21HD), with validation using tidal elevation measurements and tidal stream diamonds from... 

    Directed functional networks in Alzheimer's disease: disruption of global and local connectivity measures

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 21, Issue 4 , 2017 , Pages 949-955 ; 21682194 (ISSN) Afshari, S ; Jalili, M ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    Abstract
    Techniques available in graph theory can be applied to signals recorded from human brain. In network analysis of EEG signals, the individual nodes are EEG sensor locations and the edges correspond to functional relations between them that are extracted from EEG time series. In this paper, we study EEG-based directed functional networks in Alzheimer's disease (AD). To this end, directed connectivity matrices of 25 AD patients and 26 healthy subjects are processed and a number of meaningful graph theory metrics are studied. Our data show that functional networks of AD brains have significantly reduced global connectivity in alpha and beta bands (P < 0.05). The AD brains have significantly... 

    A novel regression imputation framework for Tehran air pollution monitoring network using outputs from WRF and CAMx models

    , Article Atmospheric Environment ; Volume 187 , 2018 , Pages 24-33 ; 13522310 (ISSN) Shahbazi, H ; Karimi, S ; Hosseini, V ; Yazgi, D ; Torbatian, S ; Sharif University of Technology
    Elsevier Ltd  2018
    Abstract
    Missing or incomplete data in short or long intervals is a common problem in measuring air pollution. Severe issues may arise when dealing with missing data for time-series prediction schemes or mean analysis. This study aimed to develop a new regression imputation framework to impute missing values in the hourly air quality data set of Tehran and enhance the applicability of Tehran Air Pollution Forecasting System (TAPFS). The proposed framework was designed based on three types of features including measurements of other stations, WRF and CAMx physical models. In this framework, elastic net and neuro-fuzzy networks were efficiently combined in a two-layer structure. The framework was... 

    ECG fiducial point extraction using switching Kalman filter

    , Article Computer Methods and Programs in Biomedicine ; Volume 157 , 2018 , Pages 129-136 ; 01692607 (ISSN) Akhbari, M ; Montazeri Ghahjaverestan, N ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
    Elsevier Ireland Ltd  2018
    Abstract
    In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called “switch” is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and... 

    Economic feasibility of CO2 capture from oxy-fuel power plants considering enhanced oil recovery revenues

    , Article Energy Procedia, 19 September 2010 through 23 September 2010 ; Volume 4 , September , 2011 , Pages 1886-1892 ; 18766102 (ISSN) Khorshidi, Z ; Soltanieh, M ; Saboohia, Y ; Arab, M ; Sharif University of Technology
    2011
    Abstract
    Considering the dramatic increase of greenhouse gases concentration in the atmosphere, especially carbon dioxide, reduction of these gases seems necessary to combat global warming. Fossil fuel power plants are one of the main sources of CO2 emission and several methods are under development to capture CO2 from power plants. In this paper, CO2 capture from a natural gas fired steam cycle power plant using oxyfuel combustion technology is studied. Oxy-fuel combustion is an interesting option since CO2 concentration in the flue gas is highly increased. The Integrated Environmental Control Model (IECM) developed by Carnegie Mellon University (USA) is used to evaluate the effect of this capture...