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Independent component analysis applied to raman spectra for classification of in vitro human coronary arteries

SILVEIRA JR., Landulfo; PAULA JR., Alderico Rodrigues De; PASQUALUCCI, Carlos Augusto; PACHECO, Marcos Tadeu T.
Fonte: TAYLOR & FRANCIS INC Publicador: TAYLOR & FRANCIS INC
Tipo: Artigo de Revista Científica
Português
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95.77%
Optical diagnostic methods, such as near-infrared Raman spectroscopy allow quantification and evaluation of human affecting diseases, which could be useful in identifying and diagnosing atherosclerosis in coronary arteries. The goal of the present work is to apply Independent Component Analysis (ICA) for data reduction and feature extraction of Raman spectra and to perform the Mahalanobis distance for group classification according to histopathology, obtaining feasible diagnostic information to detect atheromatous plaque. An 830nm Ti:sapphire laser pumped by an argon laser provides near-infrared excitation. A spectrograph disperses light scattered from arterial tissues over a liquid-nitrogen cooled CCD to detect the Raman spectra. A total of 111 spectra from arterial fragments were utilized.

Detection of Auditory Cortex Activity by fMRI Using a Dependent Component Analysis

ESTOMBELO-MONTESCO, Carlos A.; STURZBECHER, Marcio Jr.; BARROS, Allan K. D.; ARAUJO, Draulio B. de
Fonte: SPRINGER-VERLAG BERLIN Publicador: SPRINGER-VERLAG BERLIN
Tipo: Artigo de Revista Científica
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Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM)...

A novel method for power quality multiple disturbance decomposition based on Independent Component Analysis

Lima, Marcelo Antonio Alves; Cerqueira, Augusto S.; Coury, Denis Vinicius; Duque, Carlos A.
Fonte: ELSEVIER SCI LTD; OXFORD Publicador: ELSEVIER SCI LTD; OXFORD
Tipo: Artigo de Revista Científica
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In this paper, a novel method for power quality signal decomposition is proposed based on Independent Component Analysis (ICA). This method aims to decompose the power system signal (voltage or current) into components that can provide more specific information about the different disturbances which are occurring simultaneously during a multiple disturbance situation. The ICA is originally a multichannel technique. However, the method proposes its use to blindly separate out disturbances existing in a single measured signal (single channel). Therefore, a preprocessing step for the ICA is proposed using a filter bank. The proposed method was applied to synthetic data, simulated data, as well as actual power system signals, showing a very good performance. A comparison with the decomposition provided by the Discrete Wavelet Transform shows that the proposed method presented better decoupling for the analyzed data. (C) 2012 Elsevier Ltd. All rights reserved.; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq); CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico); Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES); CAPES (Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior); Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (FAPEMIG); FAPEMIG (Fundacao de Amparo a Pesquisa do Estado de Minas Gerais)

Análise de componentes independentes aplicada à separação de sinais de áudio.; Independent component analysis applied to separation of audio signals.

Moreto, Fernando Alves de Lima
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 19/03/2008 Português
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Este trabalho estuda o modelo de análise em componentes independentes (ICA) para misturas instantâneas, aplicado na separação de sinais de áudio. Três algoritmos de separação de misturas instantâneas são avaliados: FastICA, PP (Projection Pursuit) e PearsonICA; possuindo dois princípios básicos em comum: as fontes devem ser independentes estatisticamente e não-Gaussianas. Para analisar a capacidade de separação dos algoritmos foram realizados dois grupos de experimentos. No primeiro grupo foram geradas misturas instantâneas, sinteticamente, a partir de sinais de áudio pré-definidos. Além disso, foram geradas misturas instantâneas a partir de sinais com características específicas, também geradas sinteticamente, para avaliar o comportamento dos algoritmos em situações específicas. Para o segundo grupo foram geradas misturas convolutivas no laboratório de acústica do LPS. Foi proposto o algoritmo PP, baseado no método de Busca de Projeções comumente usado em sistemas de exploração e classificação, para separação de múltiplas fontes como alternativa ao modelo ICA. Embora o método PP proposto possa ser utilizado para separação de fontes, ele não pode ser considerado um método ICA e não é garantida a extração das fontes. Finalmente...

Aplicação da análise de componentes independentes em estudo de eventos em finanças; Independent component analysis application on events study in finance

Franco, Alexandre Lerch
Fonte: Universidade Federal do Rio Grande do Sul Publicador: Universidade Federal do Rio Grande do Sul
Tipo: Tese de Doutorado Formato: application/pdf
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Nas últimas duas décadas, estudos empíricos em finanças têm utilizado o método de estudo de eventos para detectar retornos anormais no entorno de eventos que, teoricamente, deveriam ser incorporados instantaneamente no preço dos títulos. O método de estudo de eventos, a partir da década de 90, com a massificação das planilhas eletrônicas e dos pacotes estatísticos, se popularizou no meio acadêmico brasileiro, sendo um dos principais métodos de pesquisa em finanças com ênfase em mercado de capitais ou finanças corporativas. Apesar da eficácia do método em detectar a anormalidade dos retornos, comprovada em diversos estudos empíricos, acredita-se que o método seja pouco eficiente em determinar a verdadeira amplitude do retorno anormal, uma vez que são necessários pressupostos estatísticos e argumentos econômico-financeiros que podem não ser válidos. O fato de que cada modelo apresenta um desempenho diferente de captura dos retornos anormais contribui com a tese de que os modelos utilizados atualmente não conseguem filtrar totalmente o retorno anormal da série normal. Portanto, este estudo teve como objetivo principal testar a aplicabilidade do método de Análise de Componentes Independentes - ICA - em detectar retornos anormais em séries temporais e comparar o seu desempenho com os modelos geradores de retornos anormais mais utilizados em testes empíricos. Com este objetivo...

Applications of independent component analysis to the attenuation of multiple reflections in seismic data = : Aplicações da análise de componentes independentes à atenuação de reflexões múltiplas em dados sísmicos; Aplicações da análise de componentes independentes à atenuação de reflexões múltiplas em dados sísmicos

Carlos Alberto da Costa Filho
Fonte: Biblioteca Digital da Unicamp Publicador: Biblioteca Digital da Unicamp
Tipo: Dissertação de Mestrado Formato: application/pdf
Publicado em 15/03/2013 Português
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As reflexões de ondas sísmicas na subsuperfície terrestre podem ser colocadas em duas categorias disjuntas: reflexões primárias e múltiplas. Reflexões primárias carregam informações pontuais sobre um refletor específico, enquanto reflexões múltiplas carregam informações sobre interfaces e pontos de reflexão variados. Consequentemente é usual tentar atenuar reflexões múltiplas e trabalhar somente com reflexões primárias. Neste trabalho, a teoria de ondas acústicas é desenvolvida somente a partir da equação da onda. Um resultado que demonstra como a propagação de ondas acústicas pode ser descrita somente com uma única multiplicação por matriz é exposta. Este resultado permite que um algoritmo seja desenvolvido que, em teoria, pode ser usado para remover todas as reflexões múltiplas que refletiram na superfície pelo menos uma vez. Uma implementação prática deste algoritmo é mostrada. Por conseguinte, a teoria de análise de componentes independentes é apresentada. Suas considerações teóricas e práticas são abordadas. Finalmente, ela é usada em conjunção com o método de eliminação de múltiplas de superfície para atenuar múltiplas de quatro dados diferentes. Estes resultados são então analisados e a eficácia do método é avaliada.; The reflections of seismic waves in the subsurface of the Earth can be placed under two disjoint categories: primary and multiple reflections. Primary reflections carry pointwise information about a specific reflector while multiple reflections carry informations about various interfaces and reflection points. Consequently...

An exploration of eliminating cross-talk in surface electromyography using independent component analysis

Howard, Róisín M; Conway, Richard; Harrison, Andrew J.
Fonte: 26th Irish Signals and Systems Conference Publicador: 26th Irish Signals and Systems Conference
Tipo: info:eu-repo/semantics/conferenceObject; all_ul_research; ul_published_reviewed
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peer-reviewed; The purpose of this study was to explore the use of Independent Component Analysis (ICA) on surface Electromyography (EMG) data to distinguish between individual muscle activations due to its capabilities for signal separation. EMG data was gathered on seven participants using the Delsys Trigno Wireless EMG system. Participants performed specific movements which targeted the calves muscle group of the lower leg. EMG sensors were attached according to SENIAM recommendations and extra sensors were attached in non-recommended locations to achieve crosstalk. Signals were acquired using proprietary Delsys software and processed using the ICA algorithm in Matlab to explore crosstalk. Integrated EMG was calculated for all results using custom Matlab code. The results showed moderate levels of agreement between the mixed signals and the original signals (p < 0.01). However, further work is needed to determine the usefulness of the independent components.

Interpreting variability in global SST data using independent component analysis and principal component analysis

Westra, S.; Brown, C.; Lall, U.; Koch, I.; Sharma, A.
Fonte: John Wiley & Sons Ltd Publicador: John Wiley & Sons Ltd
Tipo: Artigo de Revista Científica
Publicado em //2010 Português
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Component extraction techniques are used widely in the analysis and interpretation of high-dimensional climate datasets such as global sea surface temperatures (SSTs). Principal component analysis (PCA), a frequently used component extraction technique, provides an orthogonal representation of the multivariate dataset and maximizes the variance explained by successive components. A disadvantage of PCA, however, is that the interpretability of the second and higher components may be limited. For this reason, a Varimax rotation is often applied to the PCA solution to enhance the interpretability of the components by maximizing a simple structure. An alternative rotational approach is known as independent component analysis (ICA), which finds a set of underlying ‘source signals’ which drive the multivariate ‘mixed’ dataset. Here we compare the capacity of PCA, the Varimax rotation and ICA in explaining climate variability present in globally distributed SST anomaly (SSTA) data. We find that phenomena which are global in extent, such as the global warming trend and the El Niño-Southern Oscillation (ENSO), are well represented using PCA. In contrast, the Varimax rotation provides distinct advantages in interpreting more localized phenomena such as variability in the tropical Atlantic. Finally...

Multivariate streamflow forecasting using independent component analysis

Westra, S.; Sharma, A.; Brown, C.; Lall, U.
Fonte: Amer Geophysical Union Publicador: Amer Geophysical Union
Tipo: Artigo de Revista Científica
Publicado em //2008 Português
Relevância na Pesquisa
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Seasonal forecasting of streamflow provides many benefits to society, by improving our ability to plan and adapt to changing water supplies. A common approach to developing these forecasts is to use statistical methods that link a set of predictors representing climate state as it relates to historical streamflow, and then using this model to project streamflow one or more seasons in advance based on current or a projected climate state. We present an approach for forecasting multivariate time series using independent component analysis (ICA) to transform the multivariate data to a set of univariate time series that are mutually independent, thereby allowing for the much broader class of univariate models to provide seasonal forecasts for each transformed series. Uncertainty is incorporated by bootstrapping the error component of each univariate model so that the probability distribution of the errors is maintained. Although all analyses are performed on univariate time series, the spatial dependence of the streamflow is captured by applying the inverse ICA transform to the predicted univariate series. We demonstrate the technique on a multivariate streamflow data set in Colombia, South America, by comparing the results to a range of other commonly used forecasting methods. The results show that the ICA-based technique is significantly better at representing spatial dependence...

Modeling multivariable hydrological series: principal component analysis or independent component analysis?

Westra, S.; Brown, C.; Lall, U.; Sharma, A.
Fonte: Amer Geophysical Union Publicador: Amer Geophysical Union
Tipo: Artigo de Revista Científica
Publicado em //2007 Português
Relevância na Pesquisa
95.86%
The generation of synthetic multivariate rainfall and/or streamflow time series that accurately simulate both the spatial and temporal dependence of the original multivariate series remains a challenging problem in hydrology and frequently requires either the estimation of a large number of model parameters or significant simplifying assumptions on the model structure. As an alternative, we propose a relatively parsimonious two-step approach to generating synthetic multivariate time series at monthly or longer timescales, by first transforming the data to a set of statistically independent univariate time series and then applying a univariate time series model to the transformed data. The transformation is achieved through a technique known as independent component analysis (ICA), which uses an approximation of mutual information to maximize the independence between the transformed series. We compare this with principal component analysis (PCA), which merely removes the covariance (or spatial correlation) of the multivariate time series, without necessarily ensuring complete independence. Both methods are tested using a monthly multivariate data set of reservoir inflows from Colombia. We show that the discrepancy between the synthetically generated data and the original data...

Enhanced tumor contrast during breast lumpectomy provided by independent component analysis of localized reflectance measures

Eguizabal Aguado, Alma; Laughney, Ashley M.; García Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Pogue, Brian William; López Higuera, José Miguel; Conde Portilla, Olga María
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
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A spectral analysis technique to enhance tumor contrast during breast conserving surgery is proposed. A set of 29 surgically-excised breast tissues have been imaged in local reflectance geometry. Measures of broadband reflectance are directly analyzed using Principle Component Analysis (PCA), on a per sample basis, to extract areas of maximal spectral variation. A dynamic selection threshold has been applied to obtain the final number of principal components, accounting for inter-patient variability. A blind separation technique based on Independent Component Analysis (ICA) is then applied to extract diagnostically powerful results. ICA application reveals that the behavior of one independent component highly correlates with the pathologic diagnosis and it surpasses the contrast obtained using empirical models. Moreover, blind detection characteristics (no training, no comparisons with training reference data) and no need for parameterization makes the automated diagnosis simple and time efficient, favoring its translation to the clinical practice. Correlation coefficient with model-based results up to 0.91 has been achieved.

Species discrimination in plasma welding spectra by means of principal and independent component analysis

Real Peña, Eusebio; Mirapeix Serrano, Jesús María; Conde Portilla, Olga María; Ruiz Lombera, Rubén; Cobo García, Adolfo; López Higuera, José Miguel
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
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Principal and Independent Component Analysis are used in this paper to provide a discrimination among those species participating in the plasma of welding spectra. This approach might be useful for spectral line identification for emission spectroscopy, especially for online welding diagnostics and laser induced breakdown spectroscopy. In this case, the feasibility of this proposal will be analyzed by means of arcwelding experiments where different plasma species will be separated by the proposed processing scheme.

Blind breast tissue diagnosis using independent component analysis of localized backscattering response

Eguizabal Aguado, Alma; Laughney, Ashley M.; García Allende, Pilar Beatriz; Krishnaswamy, Venkataramanan; Wells, Wendy A.; Paulsen, Keith D.; Brian William, Pogue; López Higuera, José Miguel; Conde Portilla, Olga María
Fonte: SPIE Society of Photo-Optical Instrumentation Engineers Publicador: SPIE Society of Photo-Optical Instrumentation Engineers
Tipo: info:eu-repo/semantics/conferenceObject; publishedVersion
Português
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A blind separation technique based on Independent Component Analysis (ICA) is proposed for breast tumor delineation and pathologic diagnosis. Tissue morphology is determined by fitting local measures of tissue reflectance to a Mie theory approximation, parameterizing the scattering power, scattering amplitude and average scattering irradiance. ICA is applied on the scattering parameters by spatial analysis using the Fast ICA method to extract more determinant features for an accurate diagnostic. Neither training, nor comparisons with reference parameters are required. Tissue diagnosis is provided directly following ICA application to the scattering parameter images. Surgically resected breast tissues were imaged and identified by a pathologist. Three different tissue pathologies were identified in 29 samples and classified as not-malignant, malignant and adipose. Scatter plot analysis of both ICA results and optical parameters where obtained. ICA subtle ameliorates those cases where optical parameter's scatter plots were not linearly separable. Furthermore, observing the mixing matrix of the ICA, it can be decided when the optical parameters themselves are diagnostically powerful. Moreover, contrast maps provided by ICA correlate with the pathologic diagnosis. The time response of the diagnostic strategy is therefore enhanced comparing with complex classifiers...

Newton-like methods for parallel independent component analysis

Shen, Hao; Hueper, Knut
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
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Independent Component Analysis (ICA) can be studied from different angles. The performance of ICA algorithms significantly depends on the choice of the contrast function and the optimisation algorithm used in obtaining the demixing matrix. In this paper w

Generalised FastICA for Independent Subspace Analysis

Shen, Hao; Hueper, Knut
Fonte: Institute of Electrical and Electronics Engineers (IEEE Inc) Publicador: Institute of Electrical and Electronics Engineers (IEEE Inc)
Tipo: Conference paper
Português
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Independent Subspace Analysis (ISA) was developed as an extension of Independent Component Analysis (ICA) when statistical independences are assumed to exist between groups of components rather than between individual components. Due to the superiority of

Extracting High Temperature Event radiance from satellite images and correcting for saturation using Independent Component Analysis

Barnie, Talfan; Oppenheimer, Clive
Fonte: Elsevier Publicador: Elsevier
Tipo: Article; published version
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This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0034425714004337?np=y#.; We present a novel method for extracting the radiance from High Temperature Events (HTEs) recorded by geostationary imagers using Independent Component Analysis (ICA). We use ICA to decompose the image cube collected by the instrument into a sum of the outer products of independent, maximally non-Gaussian time series and images of their spatial distribution, and then reassemble the image cube using only sources that appear to be HTEs. Integrating spatially gives the time series of total HTE radiance emission. In this study we test the technique on a number of simulated HTE events, and then apply it to a number of volcanic HTEs observed by the SEVIRI instrument. We find that the technique performs well on small localised eruptions and can be used to correct for saturation. The technique offers the advantage of obviating the need for a priori knowledge of the area being imaged, beyond some basic assumptions about the nature of the processes affecting radiance in the scene, namely that (i) HTE sources are statistically independent from other processes, (ii) the radiance registered at the sensor is a linear mixture of the HTE signal and those from other processes...

Detection and removal of eyeblink artifacts from EEG using wavelet analysis and independent component analysis

Pesin, Jimy
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
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95.8%
Electrical signals generated by brain activity that are measured by the electroencephalogram can be distorted by electrical activity originating from eyeblinks and eye movements. This thesis proposes a new technique to identify and remove eyeblink artifacts from EEG data. An algorithm using a combination of wavelet analysis and independent component analysis (ICA) is implemented to detect the temporal location of the eyeblink artifact and eliminate it without compromising the integrity of the primary EEG data. The discrete wavelet transform is performed on 10 second epochs of data to detect the occurrence of ocular artifact. ICA is used to separate out the independent components within the data and the temporal locations of the eyeblink are used to remove the artifact and reconstruct the EEG data without that source of distortion. The results obtained indicate that the technique implemented may be robust enough to effectively process EEG data and is capable of removing eyeblink artifacts successfully when they are prominent and the data does not contain a great deal of movement artifact. The results show an 88.68% detection rate, a false positive rate of 4.03%, and an 87.23% removal rate for all eyeblinks that were accurately detected. The statistics obtained compared favorably with work done by others in this field of investigation.

Unsupervised spectral classification of astronomical x-ray sources based on independent component analysis

Mu, Bo
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Dissertação
Português
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95.8%
By virtue of the sensitivity of the XMM-Newton and Chandra X-ray telescopes, astronomers are capable of probing increasingly faint X-ray sources in the universe. On the other hand, we have to face a tremendous amount of X-ray imaging data collected by these observatories. We developed an efficient framework to classify astronomical X-ray sources through natural grouping of their reduced dimensionality profiles, which can faithfully represent the high dimensional spectral information. X-ray imaging spectral extraction techniques, which use standard astronomical software (e.g., SAS, FTOOLS and CIAO), provide an efficient means to investigate multiple X-ray sources in one or more observations at the same time. After applying independent component analysis (ICA), the high-dimensional spectra can be expressed by reduced dimensionality profiles in an independent space. An infrared spectral data set obtained for the stars in the Large Magellanic Cloud,observed by the Spitzer Space Telescope Infrared Spectrograph, has been used to test the unsupervised classification algorithms. The least classification error is achieved by the hierarchical clustering algorithm with the average linkage of the data, in which each spectrum is scaled by its maximum amplitude. Then we applied a similar hierarchical clustering algorithm based on ICA to a deep XMM-Newton X-ray observation of the field of the eruptive young star V1647 Ori. Our classification method establishes that V1647 Ori is a spectrally distinct X-ray source in this field. Finally...

Constrained independent component analysis for non-obtrusive pulse rate measurements using a webcam

Kyal, Survi
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Tese de Doutorado
Português
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95.79%
Assessment of cardiac function of a patient is very important for understanding a patient's physiological state. Remote measurements of the cardiac pulse can provide comfortable physiological assessment by minimizing the amount of wires and cables and allowing for near continuous measurements. It has been found that state-of-the-art algorithms based on independent component analysis (ICA) suffer from a sorting problem which hinders their performance. This effect is demonstrated in this work. The automated pulse detection techniques are applied to RGB color video recordings of the facial region of a person being monitored for cardiac function in a remote sensing environment. Automated face tracking is employed to locate the region of interest and address motion artefacts. This work proposed and evaluates a novel algorithm based on constrained source separation, aka, constrained independent source separation (cICA) to accurately estimate the pulse rate of a patient by solving the sorting problem observed in the ICA based approach. The constrained optimization problem incorporates prior information and additional requirements in the form of constraints. A reference signal with a single tone frequency corresponding to a possible heart rate is fed to the cICA algorithm. This forces the output signal to match the reference signal embodying prior knowledge about an underlying IC. It is also shown that with this algorithm a near photoplethysmography (PPG) signal corresponding to the variations in blood volume in the body can be extracted. An IRB approved study encompassing 45 subjects resulted in Bland-Altman analysis with an FDA-approved finger blood volume pulse (BVP) sensor demonstrating that the proposed algorithm provides significantly improved accuracy.

Independent component analysis (ICA) applied to ultrasound image processing and tissue characterization

Lai, Di
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Dissertação
Português
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As a complicated ubiquitous phenomenon encountered in ultrasound imaging, speckle can be treated as either annoying noise that needs to be reduced or the source from which diagnostic information can be extracted to reveal the underlying properties of tissue. In this study, the application of Independent Component Analysis (ICA), a relatively new statistical signal processing tool appeared in recent years, to both the speckle texture analysis and despeckling problems of B-mode ultrasound images was investigated. It is believed that higher order statistics may provide extra information about the speckle texture beyond the information provided by first and second order statistics only. However, the higher order statistics of speckle texture is still not clearly understood and very difficult to model analytically. Any direct dealing with high order statistics is computationally forbidding. On the one hand, many conventional ultrasound speckle texture analysis algorithms use only first or second order statistics. On the other hand, many multichannel filtering approaches use pre-defined analytical filters which are not adaptive to the data. In this study, an ICA-based multichannel filtering texture analysis algorithm, which considers both higher order statistics and data adaptation...