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Computers in Cardiology (CinC)

作者： G.D. Clifford S. Nemati R. Sameni Massachusetts Institute of Technology Cambridge MA USA Harvard MIT division of Health Sciences and Technology Cambridge MA USA Laboratoire des Images et des Signaux (LIS) CNRS-UMR 5083 INPG Grenoble France Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran

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 are incorporated as before from varying the angular frequency of the dipole as a function of the inter-beat (RR) interval. The RR interval time series is generated using our previously described model whereby time-and frequency-domain heart rate (HR) and heart rate variability (HRV) characteristics can be specified. QT-HR hysteresis is simulated by coupling the Gaussian kernels associated with the T-wave in the model with a nonlinear factor related to the local HR (determined from the last n RR intervals). Morphology changes due to respiration are simulated by coupling the RR interval to the angular frequency of the dipole. We demonstrate an example of the use of this model by simulating T-Wave Alternans (TWA). The magnitude of the TWA effect is modeled as a disturbance on the T-loop of the dipole with a magnitude that differs in each of the three VCG planes. The effect is then turned on or off using a HMM. The values of the transition matrix are determined by the local heart rate, such that when the HR ramps up towards 100 BPM, the probability of observing a TWA effect rapidly but smoothly increases. In this way, no dasiasuddenpsila switching from non-TWA to TWA is observed, and the natural tendency for TWA to be associated with a critical HR-related activation level is simulate

关键词： Rhythm Hidden Markov models Heart rate Morphology Electrocardiography Kernel Frequency Heart rate variability Character generation Hysteresis

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Development of Alzheimer's Disease Recognition using Semiautomatic Analysis of Statistical Parameters based on Frequency Characteristics of Medical images

Development of Alzheimer's Disease Recognition using Semiaut...

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IEEE International Conference on signal processing and Communications, ICSPC

作者： Meysam Torabi Hassan Moradzadeh Reza Vaziri S. Mohammad Javad Razavian Reza Dehestani Ardekani Moones Rahmandoust Ali Taalimi Emad Fatemizadeh Sharif University of Technology Iran Iran University of Science and Technology Iran Biomedical Signal and Image Processing Laboratory Sharif University of Technology Iran Biomedical Signal and Image Processing Laboratory Kashan University Iran

The paper presents an effective algorithm to analyze MR-images in order to recognize Alzheimer's disease (AD) which appeared in patient's brain. The features of interest are categorized in features of the spatial domain (FSD's) and Features of the frequency domain (FFD's) which are based on the first four statistic moments of the wavelet transform. Extracted features have been classified by a multi-layer perceptron artificial neural network (ANN). Before ANN, the number of features is reduced from 44 to 12 to optimize and eliminate any correlation between them. The contribution of this paper is to demonstrate that by using the wavelet transform number of features needed for AD diagnosis has been reduced in comparison with the previous work. We achieved 79% and 100% accuracy among test set and training set respectively, including 93 MR-images.

关键词： Alzheimer's disease Character recognition image recognition image analysis Frequency biomedical imaging Artificial neural networks Wavelet transforms Medical diagnostic imaging Algorithm design and analysis

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Optimization Algorithms for Labeling Brain Sulci Based on Graph Matching

Optimization Algorithms for Labeling Brain Sulci Based on Gr...

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International Conference on Computer Vision (ICCV)

作者： Faguo Yang Frithjof Kruggel Signal and Image Processing Laboratory Department of Biomedical Engineering University of California Irvine CA USA

Graph matching techniques are widely used in pattern recognition problems such as scene description, finger print identification, or face recognition. In this paper, we put forward two optimization methods for graph matching and compare them in the context of brain sulcus identification. The first approach is based on a constraint search in a neighborhood; the second uses a genetic algorithm for optimization. Experiments demonstrate that both methods yield satisfactory identification rates, however, the second method is more general and easier to adapt to similar problems.

关键词： Labeling Optimization methods image segmentation Pattern matching Humans Brain Surface morphology Pattern recognition Genetic algorithms Object recognition

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ECG Denoising Using Parameters of ECG Dynamical Model as the States of an Extended Kalman Filter

ECG Denoising Using Parameters of ECG Dynamical Model as the...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Omid Sayadi Reza Sameni Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran

In this paper an efficient filtering 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.

关键词： Electrocardiography Noise reduction Equations Nonlinear dynamical systems Noise measurement Filtering signal generators biomedical measurements Pollution measurement Nonlinear systems

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ECG baseline correction with adaptive bionic wavelet transform

ECG baseline correction with adaptive bionic wavelet transfo...

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International Symposium on signal processing and Its Applications (ISSPA)

作者： Omid Sayadi Mohammad Bagher Shamsollahi Biomedical Signal and Image Processing Laboratory BiSIPL School of Electrical Engineering Sharif University of Technology Tehran Iran

ISBN: (纸本)9781424407781

We have presented a new method for ECG baseline correction using the adaptive bionic wavelet transform (BWT). In fact by the means of BWT, the resolution in the time-frequency domain can be adaptively adjusted not only by the signal frequency but also by the signal instantaneous amplitude and its first-order differential. Besides by optimizing the BWT parameters parallel to modifying our previous thresholding rule, one can handle ECG baseline correction. First an estimation of the baseline wandering frequency is obtained and then the adaptation can be used only in three successive scales in which the mid-scale has the closest center frequency to the estimated frequency. Thus the implementation is possibly time consuming. Preliminary tests of BWT application to various ECG signals were constructed on the signals of MIT-BIH database which showed high performance of baseline correction, specially the procedure has largely proved advantageous over wavelet-based methods for baseline wandering cancellation.

关键词： Electrocardiography Wavelet transforms Frequency estimation Muscles signal processing Heart beat Humans signal processing algorithms Wavelet analysis Morphology

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A Novel Ensemble Strategy for Classification of Prostate Cancer Protein Mass Spectra

A Novel Ensemble Strategy for Classification of Prostate Can...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.15

作者： Amin Assareh Mohammad Hassan Moradi Vahid Esmaeili Biomedical Engineering Amirkabir University of Technology슠 Tehran Iran Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran

Protein mass spectra pattern recognition is a new forum, in which many machine learning algorithms have been conducted to enhance the chance of early cancer diagnosis. The high-dimensionality-small-sample (HDSS) problem of cancer proteomic datasets still requires more sophisticated approaches to improve the classification accuracy. In this study we present a simple ensemble strategy based on measuring the generalizing capability of different subsets of training data and apply it in making final decision. Using a limited number of biomarkers along with 5 classification algorithms, the proposed method achieved a promising performance over a well-known prostate cancer mass spectroscopy dataset.

关键词： Prostate cancer Proteins Machine learning algorithms Proteomics Biomarkers Cancer detection Classification algorithms Mass spectroscopy Data mining Pattern recognition

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Designing a Fuzzy Rule Based System to Estimate Depth of Anesthesia

Designing a Fuzzy Rule Based System to Estimate Depth of Ane...

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IEEE Symposium on Computational Intelligence and Data Mining (CIDM)

作者： V. Esmaeili A. Assareh M. B. Shamsollahi M. H. Moradi N. M. Arefian Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Iran Amirkabir University of Technology슠 Iran Department of Anesthesia Shahid Beheshti University Iran

Estimating the depth of anesthesia (DOA) is still a challenging area in anesthesia research. The objective of this study was to design a fuzzy rule based system which integrates electroencephalogram (EEG) features to quantitatively estimate the DOA. The proposed method is based on the analysis of single-channel EEG using frequency and time domain features as well as Shannon entropy measure. The fuzzy classifier is trained with features obtained from four subsets of data comprising well-defined anesthesia states: awake, moderate, general anesthesia, and isoelectric. The classifier extracts efficient fuzzy if-then rules and the DOA index is derived between 100 (full awake) to 0 (isoelectric) using fuzzy inference engine. To validate the proposed method, a clinical study has conducted on 22 patients to construct 4 subsets of reference states and also to compare the results with CSM monitor (Danmeter, Denmark), which has revealed satisfactory correlation with clinical assessments

关键词： Fuzzy systems Knowledge based systems Anesthesia Direction of arrival estimation Electroencephalography Frequency domain analysis Time domain analysis Entropy Frequency measurement Time measurement

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Classifying Depth of Anesthesia Using EEG Features, a Comparison

Classifying Depth of Anesthesia Using EEG Features, a Compar...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.10

作者： Vahid Esmaeili Mohammad Bagher Shamsollahi Noor Mohammad Arefian Amin Assareh Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Iran Department of Anesthesia Shahid Beheshti University Iran Biomedical Engineering Faculty of Amirkabir University of Technology Iran

Various EEG features have been used in depth of anesthesia (DOA) studies. The objective of this study was to find the excellent features or combination of them than can discriminate between different anesthesia states. Conducting a clinical study on 22 patients we could define 4 distinct anesthetic states: awake, moderate, general anesthesia, and isoelectric. We examined features that have been used in earlier studies using single-channel EEG signal processing method. The maximum accuracy (99.02%) achieved using approximate entropy as the feature. Some other features could well discriminate a particular state of anesthesia. We could completely classify the patterns by means of 3 features and Bayesian classifier.

关键词： Anesthesia Electroencephalography Anesthetic drugs Frequency Entropy biomedical monitoring Surgery biomedical measurements Condition monitoring signal processing

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Paving the way for cross-site pooling of magnetoencephalography (MEG) data

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International Congress Series 2007年 1300卷 615-618页

作者： Weisend, M.P. Hanlon, F.M. Montaño, R. Ahlfors, S.P. Leuthold, A.C. Pantazis, D. Mosher, J.C. Georgopoulos, A.P. Hämäläinen, M.S. Aine, C.J. The MIND Institute Albuquerque NM United States Department of Radiology University of New Mexico Albuquerque NM United States Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA United States Brain Sciences Center University of Minnesota Minneapolis MN United States Signal and Image Processing Institute University of Southern California Los Angeles CA United States Los Alamos National Laboratory Los Alamos NM United States

Pooling of magnetoencephalography (MEG) data across laboratories is non-trivial because of differences in hardware, software, and environmental noise levels. To investigate these issues, we conducted a study with the same five subjects at three sites with different MEG arrays: Elekta-Neuromag Vectorview (Boston), VSM MedTech Omega 275 (Albuquerque), and 4D Neuroimaging Magnes 3600 WH (Minneapolis). Subjects were run in a simple somatosensory paradigm. Phantom data were also taken at each site. We developed software to allow comparisons of test/retest reliability across subjects, machines, and analysis methods. Preliminary analyses showed excellent test/retest results within subjects across instruments. Analysis of phantom data showed a localization error of less than 2 mm across multiple software packages. Our results show that (1) instruments from different manufacturers yield similar results for somatosensory data, and that (2) multiple software packages produce very accurate results for simple source configurations. © 2007.

关键词： Data pooling Magnetoencephalography Median nerve Methodology Site comparison

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Seizure Detection in EEG signals: A Comparison of Different approaches

Seizure Detection in EEG signals: A Comparison of Different ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hamid R. Mohseni A. Maghsoudi Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory BiSIPL Sharif University of Technology Tehran Iran

In this paper, the performance of traditional variance-based method for detection of epileptic seizures in EEG signals are compared with various methods based on nonlinear time series analysis, entropies, logistic regression, discrete wavelet transform and time frequency distributions. We noted that variance-based method in compare to the mentioned methods had the best result (100%) applied on the same database.

关键词： Electroencephalography signal detection Epilepsy signal analysis Wavelet analysis Discrete wavelet transforms Frequency Fourier transforms Logistics Performance analysis

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