Search for: factual-database
0.009 seconds

    Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis

    , Article Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference ; 2010 , Pages 5573-5576 ; 1557170X (ISSN) Razavian, S. M ; Torabi, M ; Kim, K ; Sharif University of Technology
    In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2-weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions... 

    Robust detection of premature ventricular contractions using a wave-based Bayesian framework

    , Article IEEE transactions on bio-medical engineering ; Volume 57, Issue 2 , September , 2010 , Pages 353-362 ; 15582531 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Clifford, G. D ; Sharif University of Technology
    Detection and classification of ventricular complexes from the ECG is of considerable importance in Holter and critical care patient monitoring, being essential for the timely diagnosis of dangerous heart conditions. Accurate detection of premature ventricular contractions (PVCs) is particularly important in relation to life-threatening arrhythmias. In this paper, we introduce a model-based dynamic algorithm for tracking the ECG characteristic waveforms using an extended Kalman filter. The algorithm can work on single or multiple leads. A "polargram"--a polar representation of the signal--is introduced, which is constructed using the Bayesian estimations of the state variables. The polargram... 

    IMOS: improved meta-aligner and minimap2 on spark

    , Article BMC Bioinformatics ; Volume 20, Issue 1 , 2019 ; 14712105 (ISSN) Hadadian Nejad Yousefi, M ; Goudarzi, M ; Motahari, A ; Sharif University of Technology
    BioMed Central Ltd  2019
    Background: Long reads provide valuable information regarding the sequence composition of genomes. Long reads are usually very noisy which renders their alignments on the reference genome a daunting task. It may take days to process datasets enough to sequence a human genome on a single node. Hence, it is of primary importance to have an aligner which can operate on distributed clusters of computers with high performance in accuracy and speed. Results: In this paper, we presented IMOS, an aligner for mapping noisy long reads to the reference genome. It can be used on a single node as well as on distributed nodes. In its single-node mode, IMOS is an Improved version of Meta-aligner (IM)... 

    COVID-19 diagnosis using capsule network and fuzzy c -means and mayfly optimization algorithm

    , Article BioMed Research International ; Volume 2021 , 2021 ; 23146133 (ISSN) Farki, A ; Salekshahrezaee, Z ; Mohammadi Tofigh, A ; Ghanavati, R ; Arandian, B ; Chapnevis, A ; Sharif University of Technology
    Hindawi Limited  2021
    The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are... 

    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... 

    Switching kalman filter based methods for apnea bradycardia detection from ECG signals

    , Article Physiological Measurement ; Volume 36, Issue 9 , 2015 , Pages 1763-1783 ; 09673334 (ISSN) Ghahjaverestan, N. M ; Shamsollahi, M. B ; Ge, D ; Hernandez, A. I ; Sharif University of Technology
    Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal. The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a... 

    The effect of magnesium supplementation on anthropometric indices: A systematic review and dose-response meta-analysis of clinical trials

    , Article British Journal of Nutrition ; Volume 125, Issue 6 , 2021 , Pages 644-656 ; 00071145 (ISSN) Rafiee, M ; Ghavami, A ; Rashidian, A ; Hadi, A ; Askari, G ; Sharif University of Technology
    Cambridge University Press  2021
    Abstract The aim of this study was to determine the effects of Mg supplementation on anthropometric indices consisting of body weight, waist circumference (WC), BMI and body fat percentage. In this systematic review and dose-response meta-analysis, we searched PubMed, Cochrane Library, Scopus, Web of Science and Google Scholar from databases inception up to February 2020 for relevant randomised controlled trials. Quality of evidence was evaluated using the Cochrane Collaboration Tool. All the outcomes of this meta-analysis were pooled using the random effect model. Analysis of dose-response for Mg dosage was carried out using a fractional polynomial model. The systematic review and... 

    An implementation of a CBIR system based on SVM learning scheme

    , Article Journal of Medical Engineering and Technology ; Volume 37, Issue 1 , 2013 , Pages 43-47 ; 03091902 (ISSN) Tarjoman, M ; Fatemizadeh, E ; Badie, K ; Sharif University of Technology
    Content-based image retrieval (CBIR) has been one of the most active areas of research. The retrieval principle of CBIR systems is based on visual features such as colour, texture and shape or the semantic meaning of the images. A CBIR system can be used to locate medical images in large databases. This paper presents a CBIR system for retrieving digital human brain magnetic resonance images (MRI) based on textural features and the support vector machine (SVM) learning method. This system can retrieve similar images from the database in two groups: normal and tumoural. This research uses the knowledge of the CBIR approach to the application of medical decision support and discrimination... 

    Extraction and automatic grouping of joint and individual sources in multisubject fMRI data using higher order cumulants

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 23, Issue 2 , 2019 , Pages 744-757 ; 21682194 (ISSN) Pakravan, M ; Shamsollahi, M. B ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2019
    The joint analysis of multiple data sets to extract their interdependency information has wide applications in biomedical and health informatics. In this paper, we propose an algorithm to extract joint and individual sources of multisubject data sets by using a deflation-based procedure, which is referred to as joint/individual thin independent component analysis (JI-ThICA). The proposed algorithm is based on two cost functions utilizing higher order cumulants to extract joint and individual sources. Joint sources are discriminated by fusing signals of all subjects, whereas individual sources are extracted separately for each subject. Furthermore, JI-ThICA algorithm estimates the number of...