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Wavelet Based Feature Extraction and Dimension Reduction for the Classification of Human Cardiac Electrogram Depolarization WaveformsDe Voir, Christopher S. 04 October 2005 (has links)
An essential task for a pacemaker or implantable defibrillator is the accurate identification of rhythm categories so that the correct electrotherapy can be administered. Because some rhythms cause a rapid dangerous drop in cardiac output, it is necessary to categorize depolarization waveforms on a beat-to-beat basis to accomplish rhythm classification as rapidly as possible. In this thesis, a depolarization waveform classifier based on the Lifting Line Wavelet Transform is described. It overcomes problems in existing rate-based event classifiers; namely, (1) they are insensitive to the conduction path of the heart rhythm and (2) they are not robust to pseudo-events. The performance of the Lifting Line Wavelet Transform based classifier is illustrated with representative examples.
Although rate based methods of event categorization have served well in implanted devices, these methods suffer in sensitivity and specificity when atrial, and ventricular rates are similar. Human experts differentiate rhythms by morphological features of strip chart electrocardiograms. The wavelet transform is a simple approximation of this human expert analysis function because it correlates distinct morphological features at multiple scales. The accuracy of implanted rhythm determination can then be improved by using human-appreciable time domain features enhanced by time scale decomposition of depolarization waveforms.
The purpose of the present work was to determine the feasibility of implementing such a system on a limited-resolution platform. 78 patient recordings were split into equal segments of reference, confirmation, and evaluation sets. Each recording had a sampling rate of 512Hz, and a significant change in rhythm in the recording. The wavelet feature generator implemented in Matlab performs anti-alias pre-filtering, quantization, and threshold-based event detection, to produce indications of events to submit to wavelet transformation. The receiver operating characteristic curve was used to rank the discriminating power of the feature accomplishing dimension reduction. Accuracy was used to confirm the feature choice. Evaluation accuracy was greater than or equal to 95% over the IEGM recordings.
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Single-trial classification of an EEG-based brain computer interface using the wavelet packet decomposition and cepstral analysisLodder, Shaun 12 1900 (has links)
Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / ENGLISH ABSTRACT: Brain-Computer Interface (BCI) monitors brain activity by using signals
such as EEG, EcOG, and MEG, and attempts to bridge the gap between
thoughts and actions by providing control to physical devices that range from
wheelchairs to computers. A crucial process for a BCI system is feature extraction,
and many studies have been undertaken to find relevant information
from a set of input signals.
This thesis investigated feature extraction from EEG signals using two
different approaches. Wavelet packet decomposition was used to extract information
from the signals in their frequency domain, and cepstral analysis was
used to search for relevant information in the cepstral domain. A BCI was implemented
to evaluate the two approaches, and three classification techniques
contributed to finding the effectiveness of each feature type.
Data containing two-class motor imagery was used for testing, and the BCI
was compared to some of the other systems currently available. Results indicate
that both approaches investigated were effective in producing separable
features, and, with further work, can be used for the classification of trials
based on a paradigm exploiting motor imagery as a means of control. / AFRIKAANSE OPSOMMING: ’n Brein-Rekenaar Koppelvlak (BRK) monitor brein aktiwiteit deur gebruik
te maak van seine soos EEG, EcOG, en MEG. Dit poog om die gaping
tussen gedagtes en fisiese aksies te oorbrug deur beheer aan toestelle soos
rolstoele en rekenaars te verskaf. ’n Noodsaaklike proses vir ’n BRK is die
ontginning van toepaslike inligting uit inset-seine, wat kan help om tussen verskillende
gedagtes te onderskei. Vele studies is al onderneem oor hoe om sulke
inligting te vind.
Hierdie tesis ondersoek die ontginning van kenmerk-vektore in EEG-seine
deur twee verskillende benaderings. Die eerste hiervan is golfies pakkie ontleding,
’n metode wat gebruik word om die sein in die frekwensie gebied voor
te stel. Die tweede benadering gebruik kepstrale analise en soek vir toepaslike
inligting in die kepstrale domein. ’n BRK is geïmplementeer om beide metodes
te evalueer.
Die toetsdata wat gebruik is, het bestaan uit twee-klas motoriese verbeelde
bewegings, en drie klassifikasie-tegnieke was gebruik om die doeltreffendheid
van die twee metodes te evalueer. Die BRK is vergelyk met ander stelsels
wat tans beskikbaar is, en resultate dui daarop dat beide metodes doeltreffend
was. Met verdere navorsing besit hulle dus die potensiaal om gebruik te word
in stelsels wat gebruik maak van motoriese verbeelde bewegings om fisiese
toestelle te beheer.
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Generating space-time hypotheses in complex social-ecological systemsUnknown Date (has links)
As ecosystems degrade globally, ecosystem services that support life are increasingly threatened.
Indications of degradation are occurring in the Northern Indian River Lagoon (IRL) estuary in east central
Florida. Factors associated with ecosystem degradation are complex, including climate and land use
change. Ecosystem research needs identified by the Millennium Ecosystem Assessment (MA) include the
need to: consider the social with the physical; account for dynamism and change; account for complexity;
address issues of scale; and focus on ecosystem structure and process. Ecosystems are complex, self-organizing, multi-equilibrial, non-linear, middle-number systems that exist in multiple stable states. Results found are relative to the observation and the frame of analysis, requiring multi-scaled analytical techniques. This study addresses the identified ecosystem research needs and the complexity of the associated factors given these additional constraints. Relativity is addressed through univariate analysis of dissolved oxygen as a measure of the general health of the Northern IRL. Multiple spatial levels are employed to associate social process scales with physical process scales as basin, sub-basins, and watersheds. Scan statistics return extreme value clusters in space-time. Wavelet transforms decompose time-scales of cyclical data using varying window sizes to locate change in process scales in space over time. Wavelet transform comparative methods cluster temporal process scales across space. Combined these methods describe the space-time structure of process scales in a complex ecosystem relative to the variable examined, where the highly localized results allow for connection to unexamined variables. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection
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A Wavelet Packet Based Sifting Process and Its Application for Structural Health MonitoringShinde, Abhijeet Dipak 24 August 2004 (has links)
"In this work an innovative wavelet packet based sifting process for signal decomposition has been developed and its application for health monitoring of time-varying structures is presented. With the proposed sifting process, a signal can be decomposed into its mono-frequency components by examining the energy content in the wavelet packet components of a signal, and imposing certain decomposition criteria. The method is illustrated for simulation data of a linear three degree-of-freedom spring-mass-damper system and the results are compared with those obtained using the empirical mode decomposition (EMD) method. Both methods provide good approximations, as compared with the exact solution for modal responses from a conventional modal analysis. Incorporated with the classical Hilbert transform, the proposed sifting process may be effectively used for structural health monitoring by monitoring instantaneous modal parameters of the structure for both, cases of abrupt structural stiffness loss and progressive stiffness degradation. The effectiveness of this method for practical application is evaluated by applying the methodology for experimental data and the results obtained matched with the field observations. The proposed methodology has shown better results in a comparison study which is done to evaluate performance of the proposed approach with other available SHM techniques, namely EMD technique and Continuous Wavelet Transform (CWT) method, for cases characterized by different damage scenarios and noise conditions."
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Detecting ECG late potentials using wavelet transformVai, Mang I January 2002 (has links)
University of Macau / Faculty of Science and Technology / Department of Electrical and Electronics Engineering
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Wavelet neural networks : the fusion of HC and SC for computerized physiological signal interpretation / Fusion of HC and SC for computerized physiological signal interpretationLi, Bing Nan January 2009 (has links)
University of Macau / Faculty of Science and Technology / Department of Electrical and Electronics Engineering
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Functional data mining with multiscale statistical proceduresLee, Kichun 01 July 2010 (has links)
Hurst exponent and variance are two quantities that often characterize real-life, highfrequency
observations. We develop the method for simultaneous estimation of a timechanging
Hurst exponent H(t) and constant scale (variance) parameter C in a multifractional
Brownian motion model in the presence of white noise based on the asymptotic behavior of
the local variation of its sample paths. We also discuss the accuracy of the stable and simultaneous
estimator compared with a few selected methods and the stability of computations
that use adapted wavelet filters.
Multifractals have become popular as flexible models in modeling real-life data of high
frequency. We developed a method of testing whether the data of high frequency is consistent
with monofractality using meaningful descriptors coming from a wavelet-generated multifractal
spectrum. We discuss theoretical properties of the descriptors, their computational
implementation, the use in data mining, and the effectiveness in the context of simulations,
an application in turbulence, and analysis of coding/noncoding regions in DNA sequences.
The wavelet thresholding is a simple and effective operation in wavelet domains that selects
the subset of wavelet coefficients from a noised signal. We propose the selection of this
subset in a semi-supervised fashion, in which a neighbor structure and classification function
appropriate for wavelet domains are utilized. The decision to include an unlabeled coefficient
in the model depends not only on its magnitude but also on the labeled and unlabeled
coefficients from its neighborhood. The theoretical properties of the method are discussed
and its performance is demonstrated on simulated examples.
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A wavelet-based approach to primitive feature extraction, region-based segmentation, and identification for image information miningShah, Vijay Pravin, January 2007 (has links)
Thesis (Ph.D.)--Mississippi State University. Department of Electrical and Computer Engineering. / Title from title screen. Includes bibliographical references.
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Classificação de falhas em maquinas eletricas usando redes neurais, modelos wavelet e medidas de informaçãoSilva, Lyvia Regina Biagi 21 February 2014 (has links)
CAPES; CNPq / Este trabalho apresenta uma proposta de metodologia para detecção e classificação de falhas em motores de indução trifásicos ligados diretamente à rede elétrica. O método proposto é baseado na análise dos sinais de corrente do estator, com e sem a presença de falhas nos rolamentos, estator e rotor. Um dos efeitos desses tipos de falhas é o aparecimento de componentes de frequência específicas, relacionados à velocidade de rotação da máquina. Os sinais foram analisados usando a decomposição wavelet-packet, que permite a avaliação dos sinais em bandas de frequência de tamanhos variáveis. A partir dessa decomposição, aplicaram-se medidas de previsibilidade, como entropia relativa, potência de previsão e variância de erro normalizada, obtida com a análise de componentes previsíveis. Com essas medidas, foi possível verificar quais componentes da decomposição são mais previsíveis. Neste trabalho, a variância de erro normalizada e a potência de previsão foram utilizadas como entradas para três topologias de redes neurais artificiais classificadoras: perceptron multicamadas, redes de funções de base radial e mapas auto-organizáveis de Kohonen. Foram testados seis diferentes vetores de entrada para as redes neurais, utilizando medidas de previsibilidade e número de elementos dos vetores variados. Os ensaios foram realizados considerando amostras de sinal de diferentes motores, com vários tipos de falha, operando sob diversos regimes de torque e condições de desequilíbrio de tensão. Primeiramente, os sinais foram classificados em dois padrões: com e sem a presença de falhas. Posteriormente, detectou-se o tipo de falha presente nos sinais: rolamento, estator ou rotor. Por último, as amostras foram classificadas dentro do subgrupo de falha em que estavam presentes. / This work presents a methodology for diagnosis and classification of faults in three-phase induction motors connected directly to the power grid. The proposed method is based on the analysis of the stator current signals, with and without the presence of faults in the bearings, stator and rotor. These faults cause the presence of specific frequency components that are related to the machine rotational speed. The signals were analyzed using wavelet-packet decomposition, which allows a multiresolution evaluation of the signals. Using this decomposition, we estimated some predictability measures, such as relative entropy, predictive power and normalized error variance, obtained with the predictability component analysis. With this measures, we verified which were the most predictable components. In this work, normalized error variance and the predictive power were used as inputs to three topologies of artificial neural networks used as classifiers: multilayer perceptron, radial basis function and Kohonen self-organizing maps. We tested six different input vectors to the artificial neural networks, in which we vary the predictability measures and the number of elements of the vectors. The studies were performed considering samples of signals from different motors, with various kinds of faults, working under several load conditions and with voltage unbalance. The signals were firstly classified in two patterns: with and without the presence of faults. After, we detected the kind of fault was present in the signal: bearing, stator or rotor fault. Last, the samples were classified inside the subgroup in which they were.
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Classificação de eventos em redes de distribuição de energia elétrica utilizando modelos neurais autônomosLazzaretti, André Eugênio 06 July 2010 (has links)
Este trabalho apresenta uma metodologia para classificação de eventos de curto-circuito e mano-bras em redes de distribuição de energia elétrica, com base nos registros oscilográficos de tensão na barra da subestação de distribuição. São apresentados os resultados obtidos para duas formas distintas de pré-processamento dos sinais de tensão, sendo a primeira baseada em Transformada de Fourier e a segunda em Transformada Wavelet para diferentes famílias de funções wavelet. Foram comparados três modelos neurais para o processo de classificação: Multi-Layer Perceptron, Radial Basis Function e Support Vector Machine. Os modelos foram treinados levando em conta uma característica autônoma de operação das redes, ou seja, a seleção automática do modelo e o controle de complexidade. Os resultados foram validados para um conjunto de simulações realizadas no programa Alternative Transient Program, visando a aplicação prática do método proposto em um equipamento registrador de oscilografias, desenvolvido pelo Lactec em conjunto com a Copel - Curitiba, PR, denominado Power Quality Monitor. Foram obtidos resultados com desempenho na ordem de 90% de acerto médio para as diferentes formas de pré-processamento e diferente modelos neurais. / This work presents a method for automatic classification of faults and events related to quality of service in power distribution networks, based on oscillographies of the bar feeder voltages of the distribution substation. We present the results for two distinct pre-processing forms of the voltage signals. The first is based on the Fourier Transform and the second on the Wavelet Transform for different families of wavelet functions. We compared three neural models for the process of classification: Multi-Layer Perceptron, Radial Basis Function and Support Vector Machine. The models were trained taking into account the autonomous operation of networks, i.e. automatic model selection and control complexity. The results were validated for a set of simulations performed using the Alternative Transient Program, aimed at practical implementation of the proposed method in an oscillograph logger, developed by Lactec together with Copel, called the Power Quality Monitor. The results were obtained with performance on the order of 90% of average accuracy for the various pre-processing forms and neural models.
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