Analyse der Veränderungen von Wavelet-transformierten elektromyographischen Signalen, wie sie beim Tragen einer Kniebandage entstehen

Der Vergleich von diversen Elektromyogrammen stellt eine wesentliche Anforderung an die Datenanalyse dar. Das Ziel der vorliegenden Arbeit ist es, eine Methode mit geringem mathematischem Aufwand vorzustellen, mit der kleine Veränderungen am Bewegungsapparat durch Auswertung des EMG-Signals einfach und mit einer hohen Sensitivität ermittelt und dargestellt werden können. Die Wavelettransformierten Elektromyogramme bilden Intensitätsbilder, die in einem Bildraum als Punkte dargestellt werden können. Die Distanz-analyse der Bildpunkte im Bildraum erlaubt es festzustellen, ob zwei Gruppen von Elektromyogrammen - im vorliegenden Falle diejenigen, die beim Gehen mit und ohne Kniebandage gemessen wurden - sich im Mittel signifikant unterscheiden. Die Methode definiert eine Distanz-Winkel-Darstellung und Differenz-Intensitätsbilder, die es erlauben, die Auftrennung optisch zu beurteilen. Es ist zu erwarten, daβ bei gröβeren Interventionen die Unterschiede deutlicher erscheinen werden. The comparison of electromyograms represents a challenge for data analysis. The aim of the project was to present a method that uses a minimal computational effort to resolve small but significant changes in the muscular activity that occur while walking with and without a knee brace. The wavelet transformed electromyograms were represented as intensity patterns that resolve the power of the signal in time and frequency. The intensity pattern of each electromyogram defines single points in a pattern space. The distance between these points in pattern space were used to detect and show the separation between the groups of electromyograms that were recorded while walking with and without a knee brace. The method proposes a distance versus angle representation to visually discriminate the intensity patterns. Once it has been shown that the differences are statistically significant, one can visualize the result in a difference intensity pattern that indicates at what time and at what frequency the electromyograms vary between the two conditions tested. It is to be expected that interventions that are more intrusive than a knee brace will reveal even more distinct difference

A Finite Element Model Approach to Determine the Influence of Electrode Design and Muscle Architecture on Myoelectric Signal Properties.

INTRODUCTION: Surface electromyography (sEMG) is the measurement of the electrical activity of the skeletal muscle tissue detected at the skin's surface. Typically, a bipolar electrode configuration is used. Most muscles have pennate and/or curved fibres, meaning it is not always feasible to align the bipolar electrodes along the fibres direction. Hence, there is a need to explore how different electrode designs can affect sEMG measurements. METHOD: A three layer finite element (skin, fat, muscle) muscle model was used to explore different electrode designs. The implemented model used as source signal an experimentally recorded intramuscular EMG taken from the biceps brachii muscle of one healthy male. A wavelet based intensity analysis of the simulated sEMG signal was performed to analyze the power of the signal in the time and frequency domain. RESULTS: The model showed muscle tissue causing a bandwidth reduction (to 20-92- Hz). The inter-electrode distance (IED) and the electrode orientation relative to the fibres affected the total power but not the frequency filtering response. The effect of significant misalignment between the electrodes and the fibres (60°- 90°) could be reduced by increasing the IED (25-30 mm), which attenuates signal cancellation. When modelling pennated fibres, the muscle tissue started to act as a low pass filter. The effect of different IED seems to be enhanced in the pennated model, while the filtering response is changed considerably only when the electrodes are close to the signal termination within the model. For pennation angle greater than 20°, more than 50% of the source signal was attenuated, which can be compensated by increasing the IED to 25 mm. CONCLUSION: Differences in tissue filtering properties, shown in our model, indicates that different electrode designs should be considered for muscle with different geometric properties (i.e. pennated muscles)

SpeB of Streptococcus pyogenes Differentially Modulates Antibacterial and Receptor Activating Properties of Human Chemokines

BACKGROUND: CXC chemokines are induced by inflammatory stimuli in epithelial cells and some, like MIG/CXCL9, IP-10/CXCL10 and I-TAC/CXCL11, are antibacterial for Streptococcus pyogenes. METHODOLOGY/PRINCIPAL FINDINGS: SpeB from S. pyogenes degrades a wide range of chemokines (i.e. IP10/CXCL10, I-TAC/CXCL11, PF4/CXCL4, GROalpha/CXCL1, GRObeta/CXCL2, GROgamma/CXCL3, ENA78/CXCL5, GCP-2/CXCL6, NAP-2/CXCL7, SDF-1/CXCL12, BCA-1/CXCL13, BRAK/CXCL14, SRPSOX/CXCL16, MIP-3alpha/CCL20, Lymphotactin/XCL1, and Fractalkine/CX3CL1), has no activity on IL-8/CXCL8 and RANTES/CCL5, partly degrades SRPSOX/CXCL16 and MIP-3alpha/CCL20, and releases a 6 kDa CXCL9 fragment. CXCL10 and CXCL11 loose receptor activating and antibacterial activities, while the CXCL9 fragment does not activate the receptor CXCR3 but retains its antibacterial activity. CONCLUSIONS/SIGNIFICANCE: SpeB destroys most of the signaling and antibacterial properties of chemokines expressed by an inflamed epithelium. The exception is CXCL9 that preserves its antibacterial activity after hydrolysis, emphasizing its role as a major antimicrobial on inflamed epithelium

Gender dependent EMGs of runners resolved by time/frequency and principal pattern analysis

A promising approach for the analysis of surface electromyograms is to use wavelets to determine the spectral distribution of the signal intensity at any time. The authors have recently proposed using non-linearly scaled wavelets to obtain intensity patterns, which reflect the spectral distribution at any given time point. Further analysis of intensity-patterns is greatly facilitated by representing them as linear combinations of a base set of principal-patterns. The weight with which each principal-pattern contributes to the intensity-pattern can be represented on a set of orthogonal axes that span a previously introduced pattern space. The purpose of the present study was to show how to use pattern space to discriminate and classify male and female runners based on the electromyograms of five muscles of the limb. The results showed that there were significant gender specific differences, which allowed more than a 95% correct classification of the subjects as males or females. Classification was possible irrespective of the shod condition while running. Gender specific differences occurred at well-defined time periods during the movement. Common to both genders was that spectral changes did not parallel the changes in total signal intensity

Estimation of the interplay between groups of fast and slow muscle fibers of the tibialis anterior and gastrocnemius muscle while running

Electromyograms recorded from the lower limbs of humans while running were submitted to a time/frequency analysis using wavelets. The results of the wavelet analysis yielded intensity spectra at every time point during the swing and the stance phase. It was previously shown that more or less high frequency components get activated during different periods of the movement. The purpose of this study was to test to what extent the spectra can be reconstructed by a linear superposition of two generating spectra that were associated to groups of fast and slow muscle fibers. The terms fast and slow do not only refer to the conduction velocity but also to the shape of the motor unit action potential and are used to characterize the groups in a broader sense. The principal component analysis of the spectra confirmed that a two dimensional spectral space was appropriate. A parametric spectral decomposition was used to extract the generating spectra within the two dimensional spectral space. The generating spectra were in turn used to compute the power with which the groups of muscle fibers contribute to the measured spectra and thus to the overall muscular activity. The power that was obtained for the different time points during the movement reflects the biomechanically important interplay between the groups of muscle fibers while running