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Implementing wavelet transform/mixture of experts network for analysis of electrocardiogram beats

EXPERT SYSTEMS 2008年第2期25卷 150-162页

作者： Ubeyli, Elif Derya TOBB Ekonomi & Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of the ME network structure to guide model selection for classification of electrocardiogram (ECG) beats. The expectation maximization algorithm is used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ECG signals were decomposed into time-frequency representations using discrete wavelet transforms and statistical features were calculated to depict their distribution. The ME network structure was implemented for ECG beats classification using the statistical features as inputs. To improve classification accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. Five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database were classified with an accuracy of 96.89% by the ME network structure. The ME network structure achieved accuracy rates which were higher than those of the stand-alone neural network models.

关键词： mixture of experts expectation maximization algorithm classification accuracy discrete wavelet transform ECG beats classification

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Camera motion estimation using particle filters

Camera motion estimation using particle filters

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3rd International Conference on Computer Vision Theory and Applications

作者： Nikitidis, Symeon Zafeiriou, Stefanos Pitas, Ioannis Aristotle Univ Thessaloniki Dept Informat Thessaloniki 54124 Greece

ISBN: (纸本)9789898111210

In this paper a novel algorithm for estimating the parametric form of the camera motion is proposed. A novel stochastic vector field model is presented which can handle smooth motion patterns derived from long periods of stable camera movement and also can cope with rapid motion changes and periods where camera remains still. A set of rules for robust and online updating of the model parameters is also proposed, based on the expectation maximization algorithm. Finally, we fit this model in a particle filters framework, in order to predict the future camera motion based on current and prior knowledge.

关键词： camera motion estimation vector field model particle filtering expectation maximization algorithm

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An Efficient Framework for Brain Tumor Segmentation in Magnetic Resonance Images

An Efficient Framework for Brain Tumor Segmentation in Magne...

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1st International Workshop on Image Processing Theory

作者： Bourouis, Sami Hamrouni, Kamel Univ Tunis ElManar Natl Engn Sch Tunis LSTS Lab Tunis Tunisia

ISBN: (纸本)9781424433216

The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a region-based level-set approach and some conventional methods. Our proposed approach produces good results and decreases processing time. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.

关键词： Brain Tumor Segmentation MRI Region-Competition Based Level-Set expectation maximization algorithm

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GMM and HMM training by aggregated EM algorithm with increased ensemble sizes for robust parameter estimation

GMM and HMM training by aggregated EM algorithm with increas...

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33rd IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shinozaki, Takahiro Kawahara, Tatsuya Kyoto Univ Acad Ctr Comp & Media Studies Kyoto Japan

ISBN: (纸本)9781424414833

In order to compensate for the weaknesses of the expectation maximization (EM) algorithm to over-training and to improve model performance for new data, we have recently proposed aggregated EM (Ag-EM) algorithm that introduces bagging-like approach in the framework of the EM algorithm and have shown that it gives similar improvements as cross-validation EM (CV-EM) over conventional EM. However, a limitation with the experiments was that the number of multiple models used in the aggregation operation or the ensemble size was fixed to a small value. Here, we investigate the relationship between the ensemble size and the performance as well as giving a theoretical discussion with the order of the computational cost. The algorithm is first analyzed using simulated data and then applied to large vocabulary speech recognition on oral presentations. Both of these experiments show that Ag-EM outperforms CV-EM by using larger ensemble sizes.

关键词： expectation maximization algorithm ensemble training bagging sufficient statistics hidden Markov model

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Image reconstruction in electron microscopy

Image reconstruction in electron microscopy

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Conference on Image Reconstruction from Incomplete Data V

作者： Zheng, Yili Lee, Seunghee Doerschuk, Peter C. Purdue Univ W Lafayette IN 47907 USA

ISBN: (纸本)9780819472960

Maximum likelihood statistical algorithms are described for estimating the 3-D variation of the electron scattering intensity of biological objects from cryo electron microscopy images of multiple instances of the object. Three virus objects, two spherical and one helical, are considered. Solution of the maximum likelihood problem by expectation maximization algorithms or by direct maximization of the log likelihood requires large scale computing and end-to-end codesign of biological problem formulation, statistical models, algorithms, and software design and implementation have contributed to achieving practical results.

关键词： Cryo electron microscopy cryo EM heterogeneous spherical virus homogeneous spherical virus helical virus maximum likelihood statistical reconstruction 3-D reconstruction tomography nonlinear reconstruction expectation maximization algorithm

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Projection-based Scanned Image Enhancement

Projection-based Scanned Image Enhancement

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24th Internationational Conference on Digital Printing Technologies/Digital Fabrication 2008

作者： Ahmed, Mohamed Nooman Lexmark Int Inc Software Res Lexington KY USA

ISBN: (纸本)9780892082797

In this paper, we present a new system to segment and label document images by combining statistical and multiscale view of different image components. Texture of text, halftone and images are characterized by modeling the distribution of a novel intensity projection technique using a mixture of K Gaussians. Model parameters are then estimated using the expectation maximization (EM) algorithm. Using the proposed algorithm, halftone areas were successfully differentiated from text regions

关键词： expectation maximization algorithm

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Robust Object Detection and Tracking Using a Space-Temporal Mutual Feedback Scheme

Robust Object Detection and Tracking Using a Space-Temporal ...

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International Symposium on Knowledge Acquisition and Modeling

作者： Li, Xuchao Bian, Suxuan Jinggangshan Univ Coll Informat Sci & Media Jian 343009 Jiangxi Peoples R China Jinggangshan Univ Coll Nursing Jian Jiangxi Peoples R China

ISBN: (纸本)9781424435296

A statistical object detection and tracking mutual feedback scheme, combining Gaussian mixture model (GMM) based on principal component analysis (PCA) and expectation maximization (EM) Kalman filter algorithm, is proposed in this paper. In space object detection stage, PCA provides compact and decorrelated feature space, the tracked object feature is statistically represented as GMM in RGB color space, objects are detected by maximum a posteriori (MAP) estimation. In temporal tracking stage, the tracked object is determined by the Bhattacharyya similarity measurement, the object position of consecutive frame is predicted by EM Kalman filter algorithm. The integration of object detection and tracking spatio-temporal mutual feedback scheme can decrease the accumulation error. We have applied the proposed method to object detection and tracking under the partial occlusion and the changes of moving speed with encouraging results.

关键词： object detection and tracking Kalman filter expectation maximization algorithm feedback

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caBIG™ VISDA: Modeling, visualization, and discovery for cluster analysis of genomic data

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BMC BIOINFORMATICS 2008年第1期9卷 1-18页

作者： Zhu, Yitan Li, Huai Miller, David J. Wang, Zuyi Xuan, Jianhua Clarke, Robert Hoffman, Eric P. Wang, Yue Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA 22203 USA NIA Bioinformat Unit RRB NIH Baltimore MD 21224 USA Penn State Univ Dept Elect Engn University Pk PA 16802 USA Childrens Natl Med Ctr Med Genet Res Ctr Washington DC 20010 USA Georgetown Univ Dept Oncol Physiol & Biophys Washington DC 20007 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC 20007 USA

Background: The main limitations of most existing clustering methods used in genomic data analysis include heuristic or random algorithm initialization, the potential of finding poor local optima, the lack of cluster number detection, an inability to incorporate prior/expert knowledge, black-box and non-adaptive designs, in addition to the curse of dimensionality and the discernment of uninformative, uninteresting cluster structure associated with confounding variables. Results: In an effort to partially address these limitations, we develop the VIsual Statistical Data Analyzer (VISDA) for cluster modeling, visualization, and discovery in genomic data. VISDA performs progressive, coarse-to-fine ( divisive) hierarchical clustering and visualization, supported by hierarchical mixture modeling, supervised/unsupervised informative gene selection, supervised/unsupervised data visualization, and user/prior knowledge guidance, to discover hidden clusters within complex, high-dimensional genomic data. The hierarchical visualization and clustering scheme of VISDA uses multiple local visualization subspaces (one at each node of the hierarchy) and consequent subspace data modeling to reveal both global and local cluster structures in a "divide and conquer" scenario. Multiple projection methods, each sensitive to a distinct type of clustering tendency, are used for data visualization, which increases the likelihood that cluster structures of interest are revealed. Initialization of the full dimensional model is based on first learning models with user/prior knowledge guidance on data projected into the low-dimensional visualization spaces. Model order selection for the high dimensional data is accomplished by Bayesian theoretic criteria and user justification applied via the hierarchy of low-dimensional visualization subspaces. Based on its complementary building blocks and flexible functionality, VISDA is generally applicable for gene clustering, sample clustering, and phenoty

关键词： expectation maximization algorithm Minimum Description Length Locality Preserve Projection Emerin Discriminative Gene

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Robust unsupervised detection of action potentials with probabilistic models

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2008年第4期55卷 1344-1354页

作者： Benitez, Raul Nenadic, Zoran Univ Calif Irvine Dept Biomed Engn Irvine CA 92697 USA Univ Calif Irvine Dept Elect Engn Irvine CA 92697 USA

We develop a robust and fully unsupervised algorithm for the detection of action potentials from extracellularly recorded data. Using the continuous wavelet transform allied to probabilistic mixture models and Bayesian probability theory, the detection of action potentials is posed as a model selection problem. Our technique provides a robust performance over a wide range of simulated conditions, and compares favorably to selected supervised and unsupervised detection techniques.

关键词： action potentials Bayesian probability theory continuous wavelet transform expectation maximization algorithm finite mixture models maximum likelihood principle receiver operating characteristic unsupervised detection

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MAP channel-estimation-based PIC receiver for downlink MC-CDMA systems

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2008年第1期000卷 570624-570624页

作者： Dogan, Hakan Panayirci, Erdal Cirpan, Hakan A. Fleury, Bernard H. Istanbul Univ Dept Elect & Elect Engn TR-34850 Istanbul Turkey Kadir Kas Univ Dept Elect Engn TR-34083 Istanbul Turkey Aalborg Hosp Dept Elect Syst Sect Navig & Commun DK-9000 Aalborg Denmark

We propose a joint MAP channel estimation and data detection technique based on the expectation maximization (EM) method with paralel interference cancelation (PIC) for downlink multicarrier (MC) code division multiple access (CDMA) systems in the presence of frequency selective channels. The quality of multiple access interference (MAI), which can be improved by using channel estimation and data estimation of all active users, affects considerably the performance of PIC detector. Therefore, data and channel estimation performance obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. The EM algorithm derived estimates the complex channel parameters of each subcarrier iteratively and generates the soft information representing the data a posterior probabilities. The soft information is then employed in a PIC module to detect the symbols efficiently. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach is studied in terms of bit-error rate (BER) and mean square error (MSE). Throughout the simulations, extensive comparisons with previous works in literature are performed, showing that the new scheme can offer superior performance.

关键词： Mean Square Error Channel Estimation expectation maximization Code Division Multiple Access expectation maximization algorithm

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