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Surrogate ECG signals for the null-hypothesis analysis of heart monitoring

Surrogate ECG signals for the null-hypothesis analysis of he...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper introduces a class of synthetic electrocardiograms (ECG) for the purpose of testing the sensitivity and robustness of heart-monitoring techniques in controlled and reproducible simulations. These ECG surrogates are constructed by adapting an existing standard ECG synthesizer to act as a modulator on a tachogram input, enabling null-hypothesis analysis on heart-rate variability. This establishes an empirical link between the tachogram literature and the design of heart monitoring techniques. The new methodology is demonstrated experimentally and its performance is discussed

关键词： Electrocardiography Signal analysis Heart

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Fractal segmentation of signal from noise for radio transmitter fingerprinting

Fractal segmentation of signal from noise for radio transmit...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： L. Sun W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents a study of segmentation between channel noise and turn-on transient for radio transmitter fingerprinting. The segmentation is based on a multifractal measure, which is called variance fractal dimension trajectory. Previous studies have shown that this multifractal measure is effective in nonstationary signal characterization. We employ two techniques for the variance fractal dimension implementation and evaluate them in terms of noise segmentation. An optimal window size for the fingerprinting is presented. We also present the Renyi dimension trajectory based on generalized entropy as an alternative of the variance fractal dimension measure for noise segmentation.

关键词： Fractals Radio transmitters Fingerprint recognition Entropy Noise measurement Sun data compression Laboratories Signal analysis

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Signal separation by independent component analysis and fuzzy estimators

Signal separation by independent component analysis and fuzz...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： M. Potter W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Independent component analysis (ICA) is a developing field of interest for researchers in signal processing and artificial neural networks. ICA is an "intelligent signal processing" extension to the principal component analysis that becomes sensitive to non-Gaussian higher-order statistics. This paper presents the motivation of ICA and a treatment of the theory with a guiding example. The limitations of current ICA algorithms are discussed in general and the possible benefits of developing fuzzy engines as ICA estimators are discussed. In particular, a Mamdani-type fuzzy inference system for determining an optimal ICA rotation of whitened two-dimensional uniform noise is implemented as an example of the feasibility of this new direction in ICA.

关键词： Source separation Independent component analysis Signal processing algorithms Artificial neural networks Artificial intelligence Principal component analysis Higher order statistics Inference algorithms Engines Fuzzy systems

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Multifractal analysis of transients in power systems

Multifractal analysis of transients in power systems

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： J. Chen W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents the variance fractal dimension trajectory (VFDT) approach to studying nonstationary transients in power system. Experiments show that multifractal analysis based on the VFDT is efficient for characterizing most of the transient types found in power system signals and data reduction can be realized by simply changing the window displacement. In addition, analysis of transient signals contaminated by noise shows that the VFDT approach is very immune to noise.

关键词： Transient analysis Power system transients Fractals Power system analysis computing Discrete wavelet transforms Signal analysis Electromagnetic transients data compression Capacitors Fourier transforms

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Multifractal characterization of electromyogram signals

Multifractal characterization of electromyogram signals

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： T. Ehtiati W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Surface electromyography is a useful means of studying normal and abnormal muscle functions and has many applications, including pathological diagnosis and prosthetic control. Most of the studies of such electromyogram (EMG) signals are based on either the analysis of their stochastic temporal characteristics in the time domain, or the power spectrum characteristics in the frequency domain. In this paper, we present an approach to characterization and feature extraction of EMG signals. This approach is based upon the chaotic behaviour of the EMG signals and the existence of the corresponding strange attractors with low embedding dimensions. The multifractal dimensions of the strange attractors underlying the chaos provide alternative features for analyzing the EMG signals, and describe how the entropy of these strange attractors changes as the hypervolume scales used for calculating the entropy vary.

关键词： Fractals Electromyography Signal analysis Chaos Entropy Muscles Pathology Prosthetics Stochastic processes Time domain analysis

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ECG frame classification using dynamic time warping

ECG frame classification using dynamic time warping

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： B. Huang W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

Presents an electrocardiogram (ECG) frame classification technique realized by a dynamic time warping (DTW) matching technique, which has been used successfully in speech recognition. We use the DTW to classify ECG frames because ECG and speech signals have similar nonstationary characteristics. The DTW mapping function is obtained by searching the frame from its end to start. A threshold is setup for DWT matching residual either to classify an ECG frame or to add a new class. Classification and establishment of a template set are carried out simultaneously. A frame is classified into a category with a minimal residual and satisfying a threshold requirement. A classification residual of 1.33% is achieved by the DTW for a 10-minute ECG recording.

关键词： Electrocardiography Heart beat Fluctuations Rhythm Speech recognition Timing data compression Laboratories Discrete wavelet transforms Electrodes

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compression of multifractal object boundaries: a comparison of two approaches

Compression of multifractal object boundaries: a comparison ...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： S. Siddiqui A. El-Boustani W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

This paper presents a comparative study of two approaches that address the problem of compression of arbitrary object boundaries resulting from image segmentation. The first approach uses multiresolution based techniques, in which the boundary is represented compactly by two sets of descriptors and control points which are obtained by applying wavelet and fractal analysis. The midpoint displacement algorithm is then applied to the two sets of control points in order to reconstruct the boundaries with the required fractal dimension. The second approach is based on the iterated function system (IFS). In this approach, the object boundary is regarded as an attractor of an IFS having the same Renyi fractal dimension spectrum as the original boundary. The experimental results show that the multifractal object boundaries can be modelled compactly using both approaches. The compression ratio achieved for the wavelet-based approach was 285:1, while for the IFS-based approach it was 52:1. These results can be compared with state of the art image compression techniques such as JPEG2000

关键词： Fractals Image coding Image segmentation Transform coding Video compression Shape Curve fitting data compression Laboratories Signal resolution

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Feature extraction from DNA sequences by multifractal analysis

Feature extraction from DNA sequences by multifractal analys...

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

作者： H. Zhang W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

This paper presents feature extraction and estimation of multifractal measures of DNA sequences, using a multifractal methodology, and demonstrates a new scheme for identifying biological functionality, using information contained within the DNA sequences. It shows that the Renyi and Mandelbrot fractal dimension spectra may be useful techniques for extracting the information contained in the DNA sequences.

关键词： Feature extraction DNA Sequences Fractals Genomics Bioinformatics Organisms Proteins Humans Amino acids

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An Implementation of ß-Divergence for Blind Source Separation

An Implementation of ß-Divergence for Blind Source Separati...

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Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： N. Gadhok W. Kinsner Signal and Data Compression Laboratory Department of Electrical and Computer Engineering University of Manitoba Winnipeg MAN Canada

Life sustaining biomedical signal processing demands a guarantee that the results produced are accurate and precise. The separation of sources (e.g., demixing two heart signals, one from a mother, and one from a fetus) based only on observations of those mixtures, known as the blind source separation problem, is seen by researchers and scientists as a necessary preprocessing step in order to obtain uncontaminated data for analysis. A method from the field of intelligent signal processing called independent component analysis (ICA) is a promising solution to this problem. However, ICA algorithms and their implementation must be robust to interference, including outliers. Unfortunately, contamination of biomedical recordings by outliers is an unavoidable aspect in signal processing. Mihoko and Eguchi developed an outlier robust ICA algorithm, but code for this algorithm is unavailable. This paper presents a Matlab implementation of their beta-divergence for blind source separation algorithm. The implementation uses a quasi-Newton Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization, combined with an Armijo conditioned line-search, to minimize the beta-divergence between the density of the source estimates and the product of its hypothesized marginal densities to separate a mixture of statistically independent sources. The implementation is verified by repeating the source separation simulations published by Mihoko and Eguchi. In each simulation the separation results match visually to those published by Mihoko and Eguchi

关键词： Blind source separation Signal processing algorithms Independent component analysis Biomedical signal processing Robustness Heart Fetus data analysis Signal analysis Interference

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Image compression through fractal surface interpolation and wavelet compression

Image compression through fractal surface interpolation and ...

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Communications, Power and Computing Conference: WESCANEX

作者： R. Dansereau W. Kinsner Department of Electrical and Computer Engineering Signal and Data Compression Laboratory University of Manitoba Winnipeg MAN Canada

The paper presents a perceptual image representation technique based on fractal surface interpolation (FSI), This technique is motivated from the observation that images taken from the real world contain many textures that are self similar, or fractal, in nature. The fractal surface interpolation representation is then compressed using a zero tree wavelet compression subsystem with lossless entropy encoding. The fractal surface interpolation technique described relies on the extraction and reconstruction of self affine fractal surfaces with measured Hurst exponents H*. This gives statistically self similar fractal surfaces used to represent textures in a real world image. Fractional Brownian motion (fBm) through a modified midpoint displacement (MPD) algorithm provides the basis for generating these self affine fractal surfaces between interpolation points.

关键词： Image coding Fractals Surface waves Interpolation Surface reconstruction Surface texture Image representation Entropy Image reconstruction Brownian motion

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