Search for: signal-processing--computer-assisted
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    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... 

    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  

    EEG-based functional brain networks: does the network size matter?

    , Article PloS one ; Volume 7, Issue 4 , 2012 ; 19326203 (ISSN) Joudaki, A ; Salehi, N ; Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    PLOS  2012
    Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of... 

    The effect of exertion level on activation patterns and variability of trunk muscles during multidirectional isometric activities in upright posture

    , Article Spine ; Volume 35, Issue 11 , May , 2010 , Pages E443-E451 ; 03622436 (ISSN) Talebian, S ; Mousavi, S. J ; Olyaei, G. R ; Sanjari, M. A ; Parnianpour, M ; Sharif University of Technology
    STUDY DESIGN.: An experimental design to investigate activation patterns of trunk muscles during multidirectional exertions. OBJECTIVES.: To evaluate trunk muscle activation patterns in varying directions and moment magnitudes during an isometric task, and to investigate the effects of angle and level of isometric exertion on the electromyography (EMG) variability of trunk muscles in upright posture. SUMMARY OF BACKGROUND DATA.: Few studies have investigated trunk muscle activation patterns in multidirectional exertions with different moment magnitudes. METHODS.: A total of 12 asymptomatic male subjects were participated in the study. The EMG activity of 10 selected trunk muscles was... 

    A model-based Bayesian framework for ECG beat segmentation

    , Article Physiological Measurement ; Volume 30, Issue 3 , 2009 , Pages 335-352 ; 09673334 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance. ©... 

    ECG denoising using parameters of ECG dynamical model as the states of an extended Kalman filter

    , Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 2548-2551 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) Sayadi, O ; Sameni, R ; Shamsollahi, M. B ; Sharif University of Technology
    In this paper an efficient Altering procedure based on the Extended Kalman Filter (EKF) has been proposed. The method is based on a modified nonlinear dynamic model, previously introduced for the generation of synthetic ECG signals. We have suggested simple dynamics as the governing equations for the model parameters. Since we have not any observation for these new state variables, they are considered as hidden states. Quantitative evaluation of the proposed algorithm on the MIT-BIH signals shows that an average SNR improvement of 12 dB is achieved for a signal of -5 dB. The results show improved output SNRs compared to the EKF outputs in the absence of these new dynamics. © 2007 IEEE  

    Synchronization of EEG: Bivariate and multivariate measures

    , Article IEEE Transactions on Neural Systems and Rehabilitation Engineering ; Vol. 22, Issue. 2 , 2014 , pp. 212-221 ; ISSN: 1534-4320 Jalili, M ; Barzegaran, E ; Knyazeva, M. G ; Sharif University of Technology
    Synchronization behavior of electroencephalographic (EEG) signals is important for decoding information processing in the human brain. Modern multichannel EEG allows a transition from traditional measurements of synchronization in pairs of EEG signals to whole-brain synchronization maps. The latter can be based on bivariate measures (BM) via averaging over pair-wise values or, alternatively, on multivariate measures (MM), which directly ascribe a single value to the synchronization in a group. In order to compare BM versus MM, we applied nine different estimators to simulated multivariate time series with known parameters and to real EEGs. We found widespread correlations between BM and MM,... 

    All-optical controlled switching in centrally coupled circular array of nonlinear optical fibers

    , Article Applied Optics ; Volume 52, Issue 25 , Sep , 2013 , Pages 6131-6137 ; 1559128X (ISSN) Tofighi, S ; Bahrampour, A. R ; Sharif University of Technology
    Optical Society of American (OSA)  2013
    We show that, in a nonlinear centrally coupled circular array of evanescently coupled fibers, the coupling dynamics of a weak signal beam can be efficiently influenced by a high-power control beam that induces nonlinear defects. When the intense control beam is launched into the central core and one core in the periphery, then localized solitons are formed and cause the fibers with induced defects (defected fibers) to decouple from the other array elements. In the presence of a high-intensity control beam, the propagation of weak signal is restricted to the defected optical fibers. The weak signal periodically couples between the induced defects. This oscillatory behavior depends on the sign... 

    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... 

    Average consensus in networks of dynamic multi-agents with switching topology: Infinite matrix products

    , Article ISA Transactions ; Volume 51, Issue 4 , 2012 , Pages 522-530 ; 00190578 (ISSN) Atrianfar, H ; Haeri, M ; Sharif University of Technology
    This paper deals with the average consensus problem in a multi-agent system with switching interaction topology modeled as a weighted digraph. The convergence analysis is performed in both discrete-time and continuous-time dynamics based on the theory of infinite matrix products. Conditions for system convergence to average consensus are derived in the form of constraints on direct and reverse graphs and the structure of adjacency elements among the agents. Furthermore, a sufficient condition is provided for convergence to average consensus in systems in which the interaction topology is balanced over infinite contiguous non-overlapping time intervals instead of being balanced continuously.... 

    Constructing brain functional networks from EEG: Partial and unpartial correlations

    , Article Journal of Integrative Neuroscience ; Volume 10, Issue 2 , 2011 , Pages 213-232 ; 02196352 (ISSN) Jalili, M ; Knyazeva, M. G ; Sharif University of Technology
    We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial... 

    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... 

    Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

    , Article Australasian Physical and Engineering Sciences in Medicine ; Volume 39, Issue 3 , 2016 , Pages 717-726 ; 01589938 (ISSN) BagheriMofidi, S. M ; Pouladian, M ; Jameie, S. B ; Abbaspour Tehrani Fard, A ; Sharif University of Technology
    Springer Netherlands  2016
    Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000... 

    Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results

    , Article IEEE Transactions on Biomedical Engineering ; Volume 56, Issue 8 , 2009 , Pages 2075-2082 ; 00189294 (ISSN) Gan, K. B ; Zahedi, E ; Mohd Ali, M. A ; Sharif University of Technology
    In obstetrics, fetal heart rate (FHR) detection remains the standard for intrapartum assessment of fetal well-being. In this paper, a low-power (<55 mW) optical technique is proposed for transabdominal FHR detection using near-infrared photoplesthys-mography (PPG). A beam of IR-LED (890 nm) propagates through to the maternal abdomen and fetal tissues, resulting in a mixed signal detected by a low-noise detector situated at a distance of 4 cm. Low-noise amplification and 24-bit analog-to-digital converter resolution ensure minimum effect of quantization noise. After synchronous detection, the mixed signal is processed by an adaptive filter to extract the fetal signal, whereas the PPG from the... 

    The application of empirical mode decomposition for the enhancement of cardiotocograph signals

    , Article Physiological Measurement ; Volume 30, Issue 8 , 2009 , Pages 729-743 ; 09673334 (ISSN) Krupa, B. N ; Mohd Ali, M. A ; Zahedi, E ; Sharif University of Technology
    Cardiotocograph (CTG) is widely used in everyday clinical practice for fetal surveillance, where it is used to record fetal heart rate (FHR) and uterine activity (UA). These two biosignals can be used for antepartum and intrapartum fetal monitoring and are, in fact, nonlinear and non-stationary. CTG recordings are often corrupted by artifacts such as missing beats in FHR, high-frequency noise in FHR and UA signals. In this paper, an empirical mode decomposition (EMD) method is applied on CTG signals. A recursive algorithm is first utilized to eliminate missing beats. High-frequency noise is reduced using EMD followed by the partial reconstruction (PAR) method, where the noise order is... 

    Applicability of adaptive noise cancellation to fetal heart rate detection using photoplethysmography

    , Article Computers in Biology and Medicine ; Volume 38, Issue 1 , 2008 , Pages 31-41 ; 00104825 (ISSN) Zahedi, E ; Beng, G. K ; Sharif University of Technology
    In this paper, an approach based on adaptive noise cancellation (ANC) is evaluated for extraction of the fetal heart rate using photoplethysmographic signals from the maternal abdomen. A simple optical model is proposed in which the maternal and fetal blood pulsations result in emulated signals where the lower SNR limit (fetal to maternal) is - 25 dB. It is shown that a recursive least-squares algorithm is capable of extracting the peaks of the fetal PPG from these signals, for typical values of maternal and fetal tissues. © 2007 Elsevier Ltd. All rights reserved  

    Nonlinear analysis of anesthesia dynamics by fractal scaling exponent

    , 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 6225-6228 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Gifani, P ; Rabiee, H. R ; Hashemi, M. R ; Taslimi, P ; Ghanbari, M ; Sharif University of Technology
    The depth of anesthesia estimation has been one of the most research interests in the field of EEG signal processing in recent decades. In this paper we present a new methodology to quantify the depth of anesthesia by quantifying the dynamic fluctuation of the EEG signal. Extraction of useful information about the nonlinear dynamic of the brain during anesthesia has been proposed with the optimum Fractal Scaling Exponent. This optimum solution is based on the best box sizes in the Detrended Fluctuation Analysis (DFA) algorithm which have meaningful changes at different depth of anesthesia. The Fractal Scaling Exponent (FSE) Index as a new criterion has been proposed. The experimental results... 

    Introducing a new multi-wavelet function suitable for sEMG signal to identify hand motion commands

    , Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 1924-1927 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) Khezri, M ; Jahed, M ; Sharif University of Technology
    In recent years, electromyogram signal (EMG) feature selection, based on wavelet transform, has received considerable attention. This study introduces a new multiwavelet function for surface EMG (sEMG) signal intended for tasks that involve hand movement recognition. To create the new wavelet function, several types of well known mother wavelet were exploited and through their integration the proposed mother wavelet was generated. The proposed wavelet function closely reproduced the characteristics of the EMG signal, while increasing the recognition accuracy of hand movements. We used eight unique classes of hand motions and considered the ability of various mother wavelets and the proposed... 

    Utility of a nonlinear joint dynamical framework to model a pair of coupled cardiovascular signals

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 4 , 2013 , Pages 881-890 ; 21682194 (ISSN) Sayadi, O ; Shamsollahi, M. B ; Sharif University of Technology
    We have recently proposed a correlated model to provide a Gaussian mixture representation of the cardiovascular signals, with promising results in identifying rhythm disturbances. The approach provides a transformation of the data into a set of integrable Gaussians distributed over time. Looking into the model from a new joint modeling perspective, it is capable of assembling a filtered estimation, and can be used to derive temporal information of the waveforms. In this paper, we present a step-by-step derivation of the joint model putting correlation assumptions together to conclude a minimal joint description for a pair of ECG-ABP signals. We then probe novel applications of this model,... 

    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
    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...