Seleção de características para identificação de diferentes proporções de tipos de fibras musculares por meio da eletromiografia de superfície

Skeletal muscle consists of muscle fiber types that have different physiological and biochemical characteristics. Basically, the muscle fiber can be classified into type I and type II, presenting, among other features, contraction speed and sensitivity to fatigue different for each type of muscle fiber. These fibers coexist in the skeletal muscles and their relative proportions are modulated according to the muscle functionality and the stimulus that is submitted. To identify the different proportions of fiber types in the muscle composition, many studies use biopsy as standard procedure. As the surface electromyography (EMGs) allows to extract information about the recruitment of different motor units, this study is based on the assumption that it is possible to use the EMG to identify different proportions of fiber types in a muscle. The goal of this study was to identify the characteristics of the EMG signals which are able to distinguish, more precisely, different proportions of fiber types. Also was investigated the combination of characteristics using appropriate mathematical models. To achieve the proposed objective, simulated signals were developed with different proportions of motor units recruited and with different signal-to-noise ratios. Thirteen characteristics in function of time and the frequency were extracted from emulated signals. The results for each extracted feature of the signals were submitted to the clustering algorithm k-means to separate the different proportions of motor units recruited on the emulated signals. Mathematical techniques (confusion matrix and analysis of capability) were implemented to select the characteristics able to identify different proportions of muscle fiber types. As a result, the average frequency and median frequency were selected as able to distinguish, with more precision, the proportions of different muscle fiber types. Posteriorly, the features considered most able were analyzed in an associated way through principal component analysis. Were found two principal components of the signals emulated without noise (CP1 and CP2) and two principal components of the noisy signals (CP1 and CP2 ). The first principal components (CP1 and CP1 ) were identified as being able to distinguish different proportions of muscle fiber types. The selected characteristics (median frequency, mean frequency, CP1 and CP1 ) were used to analyze real EMGs signals, comparing sedentary people with physically active people who practice strength training (weight training). The results obtained with the different groups of volunteers show that the physically active people obtained higher values of mean frequency, median frequency and principal components compared with the sedentary people. Moreover, these values decreased with increasing power level for both groups, however, the decline was more accented for the group of physically active people. Based on these results, it is assumed that the volunteers of the physically active group have higher proportions of type II fibers than sedentary people. Finally, based on these results, we can conclude that the selected characteristics were able to distinguish different proportions of muscle fiber types, both for the emulated signals as to the real signals. These characteristics can be used in several studies, for example, to evaluate the progress of people with myopathy and neuromyopathy due to the physiotherapy, and also to analyze the development of athletes to improve their muscle capacity according to their sport. In both cases, the extraction of these characteristics from the surface electromyography signals provides a feedback to the physiotherapist and the coach physical, who can analyze the increase in the proportion of a given type of fiber, as desired in each case.

Avaliação da eficácia da implementação de um pacote de medidas na prevenção de pneumonia associada à ventilação mecânica em unidade de terapia intensiva adulta

Background: Ventilator-associated pneumonia (VAP) is a health care related infection and the second leading cause of nosocomial infections linked to morbidity and mortality rates. Therefore, the implementation of care guideline protocols has become necessary for critically ill patients in ICUs in order to provide adequate treatment. Objective: To assess the impact of a package called FAST HUG in PAV ; analyze the risk factors for occurrence of VAP in adult patients at an ICU of a private hospital ; analyze the clinical characteristics of patients who were or were not submitted to the FAST HUG ; analyze the etiology of microorganisms related to EPI ; determine the cost of hospitalization in patients with pneumonia and in patients who received the FAST HUG.Methods: The study was performed in a private hospital that has an 8-bed ICU. It was divided into two phases: before implementing FAST HUG, from August 2011 to August 2012 and after the implementation of FAST HUG, from September 2012 to December 2013. An individual form for each patient in the study was filled out by using information taken electronically from the hospital medical records. The following data for each patient was obtained: age, gender, reason for hospitalization, the use of three or more types of antibiotics, length of stay, intubation time and progress. Findings: After the implementation of FAST HUG, there was an observable decrease in the occurrence of VAP (p <0.01), as well as a reduction in mortality rates (p <0.01). It also shows that the intervention performed in the study resulted in a significant reduction in ICU hospital costs (p <0.05).Conclusion: The implementation of FAST HUG reduced the cases of VAP. Thus, decreasing costs, reducing mortality rates and length of stay, which therefore resulted in an improvement to the overall quality of care.