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    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
    Abstract
    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
    Abstract
    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... 

    A novel approach to recognize hand movements via sEMG patterns

    , 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 4907-4910 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) Khezri, M ; Jahed, M ; Sharif University of Technology
    2007
    Abstract
    Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected form surface of the skin has been used in diverse applications. One of its usages is exploiting it in a pattern recognition system which evaluates and synthesizes hand prosthesis movements. The ability of current prosthesis has been limited in simple opening and closing that decreases the efficacy of these devices in contrary to natural hand. In order to extend the ability and accuracy of prosthesis arm movements and performance, a novel approach for sEMG pattern recognizing system is proposed. In order to have a relevant comparison, present and recent research for... 

    Assessment of preprocessing on classifiers used in the P300 speller paradigm

    , 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 1319-1322 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mirghasemi, H ; Shamsollahi, M. B ; Fazel Rezai, R ; Sharif University of Technology
    2006
    Abstract
    Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve... 

    Sensitivity analysis of the OWA operator

    , Article IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics ; Volume 38, Issue 2 , 2008 , Pages 547-552 ; 10834419 (ISSN) Zarghami, M ; Szidarovszky, F ; Ardakanian, R ; Sharif University of Technology
    2008
    Abstract
    The successful design and application of the ordered weighted averaging (OWA) method as a decision-making tool depend on the efficient computation of its order weights. The most popular methods for determining the order weights are the fuzzy linguistic quantifiers approach and the minimal variability method, which give different behavior patterns for the OWA. These two methods will be first analyzed in detail by using sensitivity analysis on the outputs of the OWA with respect to the optimism degree of the decision maker, and then the two methods will be compared. The fuzzy linguistic quantifiers approach gives more information about the behavior of the OWA outputs in comparison to the... 

    Using distance on the Riemannian manifold to compare representations in brain and in models

    , Article NeuroImage ; Volume 239 , 2021 ; 10538119 (ISSN) Shahbazi, M ; Shirali, A ; Aghajan, H ; Nili, H ; Sharif University of Technology
    Academic Press Inc  2021
    Abstract
    Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI... 

    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
    2007
    Abstract
    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... 

    An optimization based approach embedded in a fuzzy connectivity algorithm for airway tree segmentation

    , Article Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology", 20 August 2008 through 25 August 2008, Vancouver, BC ; 2008 , Pages 4011-4014 ; 9781424418152 (ISBN) Yousefi Rizi, F ; Ahmadian, A. R ; Fatemizadeh, E ; Alirezaie, J ; Sharif University of Technology
    2008
    Abstract
    The main problem with airway segmentation methods which significantly influences their accuracy is leakage into the extra-luminal regions due to thinness of the airway wall during the process of segmentation. This phenomenon potentially makes large regions of lungparenchyma to be wrongly identified as airways. A solution to this problem in the previous methods was based on leak detection followed by reducing leakage during the segmentation process. This has been dealt with adjusting the segmentation parameters and performing the re-segmentation process on the pre-segmented area. This makes the algorithm very exhaustive and more dependent on the user interaction. The method presented here is... 

    Surface Electromyogram signal estimation based on wavelet thresholding technique

    , Article 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, 20 August 2008 through 25 August 2008 ; 2008 , Pages 4752-4755 ; 9781424418152 (ISBN) Khezri, M ; Jahed, M ; Sharif University of Technology
    IEEE Computer Society  2008
    Abstract
    Surface Electromyogram signal collected from the surface of skin is a biopotential signal that may be influenced by different types of noise. This is a considerable drawback in the processing of the sEMG signals. To acquire the clean sEMG that contains useful information, we need to detect and eliminate these unwanted parts of signal. In this work, a new method based on wavelet thresholding technique is presented which provides an acceptable purified sEMG signal. sEMG signals for this study are extracted for various hand movements. We use three hand movements to calculate the near optimal estimation parameters. In this work two types of thresholding techniques, namely Stein unbiased risk... 

    A heuristic method for finding the optimal number of clusters with application In medical data

    , Article 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, 20 August 2008 through 25 August 2008 ; 2008 , Pages 4684-4687 ; 9781424418152 (ISBN) Bayati, H ; Davoudi, H ; Fatemizadeh, E ; Sharif University of Technology
    IEEE Computer Society  2008
    Abstract
    In this paper, a heuristic method for determining the optimal number of clusters is proposed. Four clustering algorithms, namely K-means, Growing Neural Gas, Simulated Annealing based technique, and Fuzzy C-means in conjunction with three well known cluster validity indices, namely Davies-Bouldin index, Calinski-Harabasz index, Maulik-Bandyopadhyay index, in addition to the proposed index are used. Our simulations evaluate capability of mentioned indices in some artificial and medical datasets. © 2008 IEEE  

    Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model

    , Article Journal of Neuroscience Methods ; Volume 253 , 2015 , Pages 28-37 ; 01650270 (ISSN) Afzali, M ; Fatemizadeh, E ; Soltanian Zadeh, H ; Sharif University of Technology
    Abstract
    Background: Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution. New method: This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately. Results: The method is applied on the synthetic and real DWI data of control and... 

    A tale of two symmetrical tails: Structural and functional characteristics of palindromes in proteins

    , Article BMC Bioinformatics ; Volume 9 , 2008 ; 14712105 (ISSN) Sheari, A ; Kargar, M ; Katanforoush, A ; Arab, S ; Sadeghi, M ; Pezeshk, H ; Eslahchi, C ; Marashi, S. A ; Sharif University of Technology
    2008
    Abstract
    Background: It has been previously shown that palindromic sequences are frequently observed in proteins. However, our knowledge about their evolutionary origin and their possible importance is incomplete. Results: In this work, we tried to revisit this relatively neglected phenomenon. Several questions are addressed in this work. (1) It is known that there is a large chance of finding a palindrome in low complexity sequences (i.e. sequences with extreme amino acid usage bias). What is the role of sequence complexity in the evolution of palindromic sequences in proteins? (2) Do palindromes coincide with conserved protein sequences? If yes, what are the functions of these conserved segments?... 

    Real-time intelligent pattern recognition algorithm for surface EMG signals

    , Article BioMedical Engineering Online ; Volume 6 , 3 December , 2007 ; 1475925X (ISSN) Khezri, M ; Jahed, M ; Sharif University of Technology
    2007
    Abstract
    Background: Electromyography (EMG) is the study of muscle function through the inquiry of electrical signals that the muscles emanate. EMG signals collected from the surface of the skin (Surface Electromyogram: sEMG) can be used in different applications such as recognizing musculoskeletal neural based patterns intercepted for hand prosthesis movements. Current systems designed for controlling the prosthetic hands either have limited functions or can only be used to perform simple movements or use excessive amount of electrodes in order to achieve acceptable results. In an attempt to overcome these problems we have proposed an intelligent system to recognize hand movements and have provided... 

    Analysis of P300 classifiers in brain computer interface speller

    , 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 6205-6208 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mirghasemi, H ; Fazel Rezai, R ; Shamsollahi, M. B ; Sharif University of Technology
    2006
    Abstract
    In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (RSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any...