Masseter muscle activity resulting from stimulation of hypothalamic behavioral sites : wavelet analysis

Patterns of electromyographic (EMG) activity can give an insight into muscle activity associated with a given behavioral state. The masseter muscle is positioned closely to the temporomandibular joint and controls the position and movement of the jaw. The hypothalamus is the region of the brain associated with emotional behavior. In an effort to further understand the muscle activity underlying emotional display, the hypothalamus in two cats was stimulated to evoke a stereotyped emotional response, known as the rage response. Unsheathing of the claws, retraction of the ears, significant pupillary dilation and vocalization (hissing) characterize this behavior. EMG data obtained at the masseter muscle during this emotional state were compared to EMG activity recorded during mastication (eating), the simulated voluntary behavior for this study. The results of this study indicate that the emotional state significantly influences the EMG activity in the masseter muscle. This is evidenced statistically by a larger high frequency component in the EMG data. It is also evidenced by the ratio of stimulation to mastication power levels at different frequencies, which increases as frequency increases. The frequency range between 5-30 Hz has been utilized in the past in studies assessing fatigue. However, the results of this research indicate that the interpretation of the data in this frequency band must be different in studies of emotionally elicited muscle response. Recordings obtained during voluntary muscular activity reflected the typical fatigue response, and appropriate elevations in the power in the 5-30 Hz frequency range occurred, in agreement with previous findings. Recordings obtained during stimulation indicate that the highest power in this frequency band is achieved at the onset of hypothalamic stimulation, rather than at the point in time when fatigue typically occurs, in contrast to previous findings

Análise do Ruído Magnético de Barkhausen por Meio da Transformada Wavelet Discreta para Detecção de Microestrutura Fragilizante em Aço

Ensaios eletromagnéticos não destrutivos vêm sendo desenvolvidos para acompanhamento de formação de microestruturas deletérias em materiais ferromagnéticos. No presente trabalho, o ruído magnético de Barkhausen (RMB) resultante da interação entre ondas emissoras de 5 Hz, 25 Hz e 50 Hz em um aço inoxidável duplex SAF 2205 é avaliado por meio da Transformada Wavelet Discreta (TWD), uma ferramenta de processamento de sinais que possibilita exibir o comportamento de um sinal em diferentes frequências. O aço inoxidável duplex SAF 2205 é utilizado para o estudo por apresentar, após tratamento térmico, a formação de precipitados finos no interior dos grãos que levam à fragilização do mesmo. No caso do RMB, que é um sinal não-estacionário e possui características de alta frequência, a TWD se torna útil em sua análise, uma vez que a decomposição permite a análise multirresolução, em que é possível obter informações dos sinais em diferentes faixas de frequências. Através de diferentes níveis de resolução, as wavelets são capazes de analisar as componentes de alta frequência de um sinal e gerar informações características mais precisas. A primeira fase dos experimentos foi realizada utilizando-se as Transformadas Wavelets Discretas com a famílias Daubechies de ordem 1 (Db1) e cinco níveis de detalhes. Assim, foi verificado que apenas três níveis de detalhes eram necessários para prosseguir. Após isso, outras famílias foram introduzidas, Db5 e Db10, e percebeu-se que a Daubechies de maior ordem, utilizando um nível de resolução, apresentava um melhor resultado em relação as outras. Concluiu-se neste trabalho que a análise do ruído magnético de Barkhausen utilizando a técnica de processamento de sinais pelo uso da Transformada Wavelet Discreta mostrou-se eficaz na detecção da formação de precipitados nanométricos no interior do material, que levam à fragilização do mesmo. Os resultados obtidos com a utilização de ondas emissoras de 5 Hz e 25 Hz foram satisfatórios, entretanto as ondas de 50 Hz não lograram êxito para estas aplicações

Caracterização do ruído magnético de Barkhausen em aço utilizando transformada wavelet discreta para detecção do constituinte sigma

Ensaios eletromagnéticos não destrutivos vêm sendo empregados para acompanhar transformações de fases que possam interferir nas propriedades mecânicas de materiais ferromagnéticos. No presente trabalho, o ruído magnético de Barkhausen (RMB) é avaliado através da transformada wavelet discreta (TWD), já que as interações das paredes dos domínios com a presença de novas microestruturas geram picos de tensões, os quais podem ser detectados. Nesse sentido, o aço inoxidável duplex (AID) é utilizado para o estudo, pois, quando submetido a ciclos térmicos superiores a 600 ºC, sofre fragilização devido à presença de uma fase paramagnética denominada sigma, que muda a permeabilidade do material. Um ensaio é apresentado para acompanhar a formação dessa fase em um AID SAF 2205. Sob essa ótica, a primeira fase do experimento foi realizada aplicando-se a TWD com as famílias Daubechies de ordem 1 (Db1), 5 (Db5) e 10 (Db10) e seis níveis de detalhes para a frequência de 5 Hz. Assim, foi verificado que apenas dois níveis de detalhes eram necessários para prosseguir. Após isso, as mesmas famílias foram introduzidas para as frequências de 25 Hz, 50 Hz, 75 Hz e 100 Hz, e percebeu-se que há diferença na aplicação de um ou dois níveis. Concluiu-se que a análise do ruído magnético de Barkhausen utilizando a técnica de processamento de sinais pelo uso da TWD mostrou-se eficaz na detecção da formação de precipitados no interior do material, que levam à fragilização do mesmo. Os resultados obtidos com a utilização de ondas emissoras de 5 Hz, 25 Hz e 50 Hz foram satisfatórios, entretanto as ondas de 75 Hz e 100 Hz não lograram êxito para estas aplicações

Central and peripheral autonomic influences : analysis of cardio-pulmonary dynamics using novel wavelet statistical methods

The development and implementation of novel signal processing techniques, particularly with regard to applications in the clinical environment, is critical to bringing computer-aided diagnoses of disease to reality. One of the most confounding factors in the field of cardiac autonomic response (CAR) research is the influence of the coupling of respiratory oscillations with cardiac oscillations. This research had three objectives. The first was the assessment of central autonomic influence over heart rate oscillations when the pulmonary system is damaged. The second was to assess the link between peripheral and central autonomic control schema by evaluating the heart rate variability (HRV) of people who were able or unable to adapt to the use of integrated lenses for vision, specifically acconrrmodation, correction (adaptive and non-adaptive presbyopes). The third objective was the development of a wavelet-based toolset by which the first two objectives could be achieved. The first tool is a wavelet based entropy measure that quantifies the level of information by assessing not only the entropy levels, but also the distribution of the entropy across frequency bands. The second tool is a wavelet source separation (WayS) method used to separate the respiratory component from the cardiac component, thereby allowing for analysis of the dynamics of the cardiac signal without the confounding influence of the respiratory signal that occurs when the body is perturbed. With regard to hypothesis one, the entropy method was used to separate the COPD study populations with 93% classification accuracy at rest, and with 100% accuracy during exercise. Changes in COPD and control autonomic markers were evident after respiration is removed. Specifically, the LF/HF ratio slightly decreased on average from pre to post reconstruction for controls, increased on average for COPD. In healthy controls, respiration frequency is distributed across multiple bandwidths, causing large decreases in both LF and HF when removed. With respiration effect removed from COPD population, LE dominates autonomic response, indicating that the frequency is concentrated in the HF autonomic region. Decrease in variance of data set increases probability tat smaller changes can be detected in values. The theory set forth in hypothesis two was validated by the quantification of a correlation between peripheral and central autonomic influences, as evidenced by differences in oculomotor adaptability correlating with differences in HRV. Standard Deviation varies with grouping, not with age. Increasing controlled respiration frequencies resulted in adaptive presbyopes and controls displaying similar sympathetic responses, diverging from non-adaptive group. WayS reduced frequency content in ranges concurrent with breathing rate, indicating a robust analysis. The outcome of hypothesis three was the confirmation that wavelet statistical methods possess significant potential for applications in HRV. Entropy can be used in conjunction with cluster analysis to classify patient populations with high accuracy. Using the WayS analysis, the respiration effect can be removed from HRV data sets, providing new insights into autonomic alterations, both central and peripheral, in disease

On-line Structural Integrity Monitoring and Defect Diagnosis of Steam Generators Using Analysis of Guided Acoustic Waves

Integrity monitoring and flaw diagnostics of flat beams and tubular structures was investigated in this research using guided acoustic signals. The primary objective was to study the feasibility of using imbedded sensors for monitoring steam generator and heat exchanger tubing. A piezo-sensor suite was deployed to activate and collect Lamb wave signals that propagate along metallic specimens. The dispersion curves of Lamb waves along plate and tubular structures were generated through numerical analysis. Several advanced techniques were explored to extract representative features from acoustic time series. Among them, the Hilbert-Huang transform (HHT) is a recently developed technique for the analysis of non-linear and transient signals. A moving window method was introduced to generate the local peak characters from acoustic time series, and a zooming window technique was developed to localize the structural flaws. The dissertation presents the background of the analysis of acoustic signals acquired from piezo-electric transducers for structural defect monitoring. A comparison of the use of time-frequency techniques, including the Hilbert-Huang transform, is presented. It also presents the theoretical study of Lamb wave propagation in flat beams and tubular structures, and the need for mode separation in order to effectively perform defect diagnosis. The results of an extensive experimental study of detection, location, and isolation of structural defects in flat aluminum beams and brass tubes are presented. The time-frequency analysis and pattern recognition techniques were combined for classifying structural defects in brass tubes. Several types of flaws in brass tubes were tested, both in the air and in water. The techniques also proved to be effective under background/process noise. A detailed theoretical analysis of Lamb wave propagation was performed and simulations were carried out using the finite element software system ABAQUS. This analytical study confirmed the behavior of the acoustic signals acquired from the experimental studies. The results of this research showed the feasibility of on-line detection of small structural flaws by the use of transient and nonlinear acoustic signal analysis, and its implementation by the proper design of a piezo-electric transducer suite. The techniques developed in this research would be applicable to civil structures and aerospace structures