Search for: sensitivity-and-specificity
0.011 seconds
Total 76 records

    ECG denoising using modulus maxima of wavelet transform

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 416-419 ; 1557170X (ISSN) Ayat, M ; Shamsollahi, M. B ; Mozaffari, B ; Kharabian, S ; Sharif University of Technology
    ECG denoising has always been an important issue in medical engineering. The purposes of denoising are reducing noise level and improving signal to noise ratio (SNR) without distorting the signal. This paper proposes a method for removing white Gaussian noise from ECG signals. The concepts of singularity and local maxima of the wavelet transform modulus were used for analyzing singularity and reconstructing the ECG signal. Adaptive thresholding was used to remove white Gaussian noise modulus maximum of wavelet transform and then reconstruct the signal  

    Model-based Bayesian filtering of cardiac contaminants from biomedical recordings

    , Article Physiological Measurement ; Volume 29, Issue 5 , 2008 , Pages 595-613 ; 09673334 (ISSN) Sameni, R ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
    Electrocardiogram (ECG) and magnetocardiogram (MCG) signals are among the most considerable sources of noise for other biomedical signals. In some recent works, a Bayesian filtering framework has been proposed for denoising the ECG signals. In this paper, it is shown that this framework may be effectively used for removing cardiac contaminants such as the ECG, MCG and ballistocardiographic artifacts from different biomedical recordings such as the electroencephalogram, electromyogram and also for canceling maternal cardiac signals from fetal ECG/MCG. The proposed method is evaluated on simulated and real signals. © 2008 Institute of Physics and Engineering in Medicine  

    Manifold learning for ECG arrhythmia recognition

    , Article 2013 20th Iranian Conference on Biomedical Engineering, ICBME 2013 ; 2013 , Pages 126-131 Lashgari, E ; Jahed, M ; Khalaj, B ; Sharif University of Technology
    IEEE Computer Society  2013
    Heart is a complex system and we can find its function in electrocardiogram (ECG) signal. The records show high mortality rate of heart diseases. So it is essential to detect and recognize ECG arrhythmias. The problem with ECG analysis is the vast variations among morphologies of ECG signals. Premature Ventricular Contractions (PVC) is a common type of arrhythmia which may lead to critical situations and contains risk. This study, proposes a novel approach for detecting PVC and visualizing data with respect to ECG morphologies by using manifold learning. To this end, the Laplacian Eigenmaps - One of the reduction method and it is in the nonlinear category - is used to extract important... 

    Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

    , Article IRBM ; Volume 33, Issue 5-6 , December , 2012 , Pages 298-307 ; 19590318 (ISSN) Niknazar, M ; Mousavi, S. R ; Shamsollahi, M. B ; Vosoughi Vahdat, B ; Sayyah, M ; Motaghi, S ; Dehghani, A ; Noorbakhsh, S. M ; Sharif University of Technology
    Objective: Epileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats. Methods: Seizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal... 

    Detection of fast ripples using Hidden Markov Model

    , Article 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014, 26 November 2014 through 28 November 2014 ; November , 2014 , Pages 348-352 ; 9781479974177 (ISBN) Nazarimehr, F ; Montazeri, N ; Shamsollahi, M. B ; Kachenoura, A ; Wendung, F ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2014
    Studies show that High frequency oscillations (HFOs) can be used as a reliable biomarker of epileptogenic zone, thus many algorithms have been proposed to detect HFOs. Among the wide variety of HFOs, fast ripples (FRs) are important transient oscillations occurring in the frequency band ranging from 250 Hz to 600 Hz. The automatic detection of FRs can be degenerated by the presence of some 'pulse-like' events (commonly, the component of interictal epileptic spikes) associated with an increase of the signal energy in the high frequency bands, exactly as in the case of real FRs. The goal of this study is to propose a new method for automatic detection of fast ripples by using Hidden Markov... 

    Classification of asthma based on nonlinear analysis of breathing pattern

    , Article PLoS ONE ; Volume 11, Issue 1 , 2016 ; 19326203 (ISSN) Raoufy, M. R ; Ghafari, T ; Darooei, R ; Nazari, M ; Mahdaviani, S. A ; Eslaminejad, A. R ; Almasnia, M ; Gharibzadeh, S ; Mani, A. R ; Hajizadeh, S ; Sharif University of Technology
    Public Library of Science  2016
    Normal human breathing exhibits complex variability in both respiratory rhythm and volume. Analyzing such nonlinear fluctuations may provide clinically relevant information in patients with complex illnesses such as asthma. We compared the cycle-by-cycle fluctuations of inter-breath interval (IBI) and lung volume (LV) among healthy volunteers and patients with various types of asthma. Continuous respiratory datasets were collected from forty agematched men including 10 healthy volunteers, 10 patients with controlled atopic asthma, 10 patients with uncontrolled atopic asthma, and 10 patients with uncontrolled non-atopic asthma during 60 min spontaneous breathing. Complexity of breathing... 

    Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 344-347 ; 1557170X (ISSN) Kharabian, S ; Shamsollahi, M. B ; Sameni, R ; Sharif University of Technology
    Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also,... 

    MEG based classification of wrist movement

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 986-989 ; 1557170X (ISSN) ; 978-142443296-7 (ISBN) Montazeri, N ; Shamsollahi, M. B ; Hajipour, S ; Sharif University of Technology
    Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1... 

    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... 

    1H NMR based metabolic profiling in Crohn's disease by random forest methodology

    , Article Magnetic Resonance in Chemistry ; Vol. 52, issue. 7 , July , 2014 , p. 370-376 Fathi, F ; Majari-Kasmaee, L ; Mani-Varnosfaderani, A ; Kyani, A ; Rostami-Nejad, M ; Sohrabzadeh, K ; Naderi, N ; Zali, M. R ; Rezaei-Tavirani, M ; Tafazzoli, M ; Arefi-Oskouie, A ; Sharif University of Technology
    The present study was designed to search for metabolic biomarkers and their correlation with serum zinc in Crohn's disease patients. Crohn's disease (CD) is a form of inflammatory bowel disease that may affect any part of the gastrointestinal tract and can be difficult to diagnose using the clinical tests. Thus, introduction of a novel diagnostic method would be a major step towards CD treatment.Proton nuclear magnetic resonance spectroscopy ( 1H NMR) was employed for metabolic profiling to find out which metabolites in the serum have meaningful significance in the diagnosis of CD. CD and healthy subjects were correctly classified using random forest methodology. The classification model for... 

    Metabonomics based NMR in Crohn's disease applying PLS-DA

    , Article Gastroenterology and Hepatology from Bed to Bench ; Volume 6, Issue SUPPL , 2013 , Pages S82-S86 ; 20082258 (ISSN) Fathi, F ; Oskouie, A. A ; Tafazzoli, M ; Naderi, N ; Sohrabzedeh, K ; Fathi, S ; Norouzinia, M ; Nejad, M. R ; Sharif University of Technology
    Aim: The aim of this study was to search for metabolic biomarkers of Crohn's disease (CD). Background: Crohn's disease (CD) is a type of inflammatory bowel disease that causes a wide variety of symptoms. CD can influence any part of the gastrointestinal tract from mouth to anus. CD is not easily diagnosed because monitoring tools are currently insufficient. Thus, the discovery of proper methods is needed for early diagnosis of CD. Patients and methods: We utilized metabolic profiling using proton nuclear magnetic resonance spectroscopy (1HNMR) to find the metabolites in serum. Classification of CD and healthy subject was done using partial least squares discriminant analysis (PLS-DA).... 

    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... 

    Automatic segmentation of brain MRI in high-dimensional local and non-local feature space based on sparse representation

    , Article Magnetic Resonance Imaging ; Volume 31, Issue 5 , 2013 , Pages 733-741 ; 0730725X (ISSN) Khalilzadeh, M. M ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
    Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods... 

    Adaptive sparse representation for MRI noise removal

    , Article Biomedical Engineering - Applications, Basis and Communications ; Volume 24, Issue 5 , October , 2012 , Pages 383-394 ; 10162372 (ISSN) Khalilzadeh, M. M ; Fatemizadeh, E ; Behnam, H ; Sharif University of Technology
    World Scientific  2012
    Sparse representation is a powerful tool for image processing, including noise removal. It is an effective method for Gaussian noise removal by taking advantage of a fixed and learned dictionary. In this study, the variable distribution of Rician noise is reduced in magnetic resonance (MR) images by sparse representation based on reconstruction error sets. Standard deviation of Gaussian noise is used to find these errors locally. The proposed method represents two formulas for local error calculation using standard deviation of noise. The acquired results from the real and simulated images are comparable, and in some cases, better than the best Rician noise removal method due to the... 

    Diagnosing anterior cruciate ligament injuries using a knee arthrometer: Design, fabrication, and clinical evaluation

    , Article Biomedical Engineering - Applications, Basis and Communications ; Volume 23, Issue 3 , April , 2011 , Pages 181-192 ; 10162372 (ISSN) Soudbakhsh, D ; Sahraei, E ; Shirin, M. B ; Farahmand, F ; Tahmasebi, M. N ; Parnianpour, M ; Sharif University of Technology
    Every year many people suffer from knee injuries. Previous studies on patients with knee injuries have shown that about 40% of knee injuries are Ligament injuries, and about 50% of the ligament injuries are the Anterior Cruciate Ligament (ACL) injuries. Along with other methods, knee arthrometers are widely used to diagnose ACL injuries. In the current research, a knee arthrometer was designed and developed to provide a reliable and repeatable measurement of knee laxity under anteriorposterior applied loads. Testretest configurations to examine repeatability of the device resulted in less than 1.5-mm difference for more than 97% of tests under applied loads of up to 90 N. These tests... 

    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... 

    Visual acuity classification using single trial visual evoked potentials

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 982-985 ; 1557170X (ISSN) Hajipour, S ; Shamsollahi, M. B ; Abootalebi, V ; Sharif University of Technology
    Several researches have been done to identify visual system characteristics. Some of them are based on the processing of the brain signal recordings. Visual evoked potentials (VEPs) are electrical signals which are produced in response to the visual stimuli and recorded by means of electrodes placed on the head. These signals are usually characterized by the amplitude and latency of their peaks. Different types of visual stimuli and visual system characteristics can affect the shape and hence the characteristics of VEPs. In this paper, proper visual stimuli were used and VEPs were recorded in order to classify visual acuity. To achieve this goal, visual evoked potentials were recorded and... 

    Optical radiomic signatures derived from optical coherence tomography images improve identification of melanoma

    , Article Cancer Research ; Volume 79, Issue 8 , 2019 , Pages 2021-2030 ; 00085472 (ISSN) Turani, Z ; Fatemizadeh, E ; Blumetti, T ; Daveluy, S ; Moraes, A. F ; Chen, W ; Mehregan, D ; Andersen, P. E ; Nasiriavanaki, M ; Sharif University of Technology
    American Association for Cancer Research Inc  2019
    The current gold standard for clinical diagnosis of melanoma is excisional biopsy and histopathologic analysis. Approximately 15–30 benign lesions are biopsied to diagnose each melanoma. In addition, biopsies are invasive and result in pain, anxiety, scarring, and disfigurement of patients, which can add additional burden to the health care system. Among several imaging techniques developed to enhance melanoma diagnosis, optical coherence tomography (OCT), with its high-resolution and intermediate penetration depth, can potentially provide required diagnostic information noninvasively. Here, we present an image analysis algorithm, "optical properties extraction (OPE)," which improves the... 

    AntAngioCOOL: computational detection of anti-angiogenic peptides

    , Article Journal of Translational Medicine ; Volume 17, Issue 1 , 2019 ; 14795876 (ISSN) Zahiri, J ; Khorsand, B ; Yousefi, A. A ; Kargar, M. J ; Shirali Hossein Zade, R ; Mahdevar, G ; Sharif University of Technology
    BioMed Central Ltd  2019
    Background: Angiogenesis inhibition research is a cutting edge area in angiogenesis-dependent disease therapy, especially in cancer therapy. Recently, studies on anti-angiogenic peptides have provided promising results in the field of cancer treatment. Methods: A non-redundant dataset of 135 anti-angiogenic peptides (positive instances) and 135 non anti-angiogenic peptides (negative instances) was used in this study. Also, 20% of each class were selected to construct an independent test dataset (see Additional files 1, 2). We proposed an effective machine learning based R package (AntAngioCOOL) to predict anti-angiogenic peptides. We have examined more than 200 different classifiers to build... 

    Automated detection of autism spectrum disorder using a convolutional neural network

    , Article Frontiers in Neuroscience ; Volume 13 , 2020 Sherkatghanad, Z ; Akhondzadeh, M ; Salari, S ; Zomorodi Moghadam, M ; Abdar, M ; Acharya, U. R ; Khosrowabadi, R ; Salari, V ; Sharif University of Technology
    Frontiers Media S.A  2020
    Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD...