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检索条件"机构=Biomedical Signal and Image Processing Laboratory BiSIPL"

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Tracking Dynamical Transition of Epileptic EEG Using Particle Filter

Tracking Dynamical Transition of Epileptic EEG Using Particl...

引用

IEEE International Symposium on signal processing and Information Technology (ISSPIT)

作者： Hossein Mamaghanian Mohammad B. Shamsollahi Sepideh Hajipour Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran

In this work we used the Liley EEG model as a dynamical model of EEG. Two parameters of the model which are candidates for change during an epileptic seizure are defined as new states in state space representation of this dynamical model. Then SIS particle filter is applied for estimating the defined states over time using the recorded epileptic EEG as the observation of the system. A method for fast numerical solution of the nonlinear coupled equation of the model is proposed. This model is used for tracking the dynamical properties of brain during epileptic seizure. Tracking the changes of these new defined states of the model have good information about the state transition of the model (interictal/preictal/ictal) and can be used in online monitoring algorithms for predicting seizures in epilepsy.

关键词： Particle tracking Epilepsy Electroencephalography Particle filters Brain modeling Predictive models State-space methods State estimation Couplings Nonlinear equations

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

A heuristic method for finding the optimal number of cluster...

引用

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

作者： Hamidreza Bayati Heydar Davoudi Emad Fatemizadeh Biological Signal and Image Processing Laboratory (BiSIPL) Department of Electrical Engineering Sharif University of Technology Iran

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.

关键词： Indexes Clustering algorithms Simulated annealing Cancer Gene expression Partitioning algorithms Mathematical model

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Extracting Single Trial Visual Evoked Potentials Using Iterative Generalized Eigen Value Decomposition

Extracting Single Trial Visual Evoked Potentials Using Itera...

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IEEE International Symposium on signal processing and Information Technology (ISSPIT)

作者： Sepideh Hajipour Mohammad B. Shamsollahi Hossein Mamaghanian Vahid Abootalebi Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran School of Electrical Engineering Yazd University Yazd Iran

The activity generated in the brain in response to external stimulations which is named the evoked potential (EP) is typically buried in the background EEG. Because of the low signal to noise ratio of EPs, it is difficult to record single trial evoked potentials. The traditional technique which is based on ensemble averaging destroys the dynamic information of single trials. In this paper, a new method has been proposed based on generalized eigen value decomposition to extract single trial EPs from single channel EEG recordings. The extraction of the N75-P100-N135 complex in simulated and actual visual evoked potentials is mainly taken under consideration. To illustrate the effectiveness of the proposed algorithm, it is compared with the iterative ICA method.

关键词： Electroencephalography Brain modeling Independent component analysis Data mining Iterative algorithms Iterative methods Delay Testing Wiener filter Noise reduction

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Epileptic Seizure Detection using AR Model on EEG signals

Epileptic Seizure Detection using AR Model on EEG Signals

引用

Cairo International biomedical Engineering Conference, CIBEC

作者： S. R. Mousavi M. Niknazar B. Vosoughi Vahdat Biomedical Engineering Laboratory Sharif University of Technology Tehran Iran Biomedical Signal and Image Processing Laboratory Sharif University of Technology Tehran Iran

This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted based on it. These parameters are used as a feature to classify the EEG signals into Healthy, Interictal (seizure free) and Ictal (during a seizure) groups using multilayer perceptron (MLP) classifier. Correct classification scores at the range of 91% to 96% reveals the potential of our approach for epilepsy detection.

关键词： Epilepsy Brain modeling Electroencephalography signal processing signal analysis Gamma ray detection Gamma ray detectors Laboratories Wavelet analysis Robustness

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An artificial multi-channel model for generating abnormal electrocardiographic rhythms

An artificial multi-channel model for generating abnormal el...

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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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Spatial distortion correction and crystal identification for position-sensitive avalanche photodiode-based PET scanners

Spatial distortion correction and crystal identification for...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Abhijit J. Chaudhari Anand A. Joshi Yibao Wu Richard M. Leahy Simon R. Cherry Ramsey D. Badawi Department of Biomedical Engineering University of California슠Davis Davis CA USA Signal and Image Processing Institute University of Southern California Los Angeles CA USA Laboratory of Neuro Imaging University of California Los Angeles Los Angeles CA USA Department of Radiology Davis Medical Center University of California Sacramento CA USA

Position-sensitive avalanche photodiodes (PSAPDs) are gaining widespread acceptance in modern PET scanner designs, and owing to their relative insensitivity to magnetic fields, especially in those that are MRI-compatible. Flood histograms in PET scanners are used to determine the crystal of annihilation photon interaction and hence, for detector characterization and routine quality control. For PET detectors that use PSAPDs, flood histograms show a characteristic pincushion distortion when Anger logic is used for event positioning. A small rotation in the flood histogram is also observed when the detectors are placed in a magnetic field. We first present a general purpose automatic method for spatial distortion correction for flood histograms of PSAPD-based PET detectors when placed both inside and outside a MRI scanner. Analytical formulae derived for this scheme are based on a hybrid approach that combines desirable properties from two existing event positioning schemes. The rotation of the flood histogram due to the magnetic field is determined iteratively and is accounted for in the scheme. We then provide implementation details of a method for crystal identification we have previously proposed and evaluate it for cases when the PET scanner is both outside and in a magnetic field. In this scheme, Fourier analysis is used to generate a lower-order spatial approximation of the distortion-corrected PSAPD flood histogram, which we call the ‘template‘. The template is then registered to the flood histogram using a diffeomorphic iterative intensity-based warping scheme. The calculated deformation field is then applied to the segmentation of the template to obtain a segmentation of the flood histogram. A manual correction tool is also developed for exceptional cases. We present a quantitative assessment of the proposed distortion correction scheme and crystal identification method against conventional methods. Our results indicate that our proposed methods lead to a la

关键词： Positron emission tomography Floods Histograms Magnetic fields Detectors Photonic crystals Magnetic analysis Avalanche photodiodes Quality control Event detection

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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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