545 resultados para Electromyography


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The aim of this work is to contribute to the analysis and characterization of training with whole body vibration (WBV) and the resultant neuromuscular response. WBV aims to mechanically activate muscle by eliciting stretch reflexes. Generally, surface electromyography is utilized to assess muscular response elicited by vibrations. However, EMG analysis could potentially bring to erroneous conclusions if not accurately filtered. Tiny and lightweight MEMS accelerometers were found helpful in monitoring muscle motion. Displacements were estimated integrating twice the acceleration data after gravity and small postural subject adjustments contribution removal. Results showed the relevant presence of motion artifacts on EMG recordings, the high correlation between muscle motion and EMG activity and how resonance frequencies and dumping factors depended on subject and his positioning onto the vibrating platform. Stimulations at the resonant frequency maximize muscles lengthening and in turn, muscle spindle solicitation , which may produce more muscle activation. Local mechanical stimulus characterization (Le, muscle motion analysis) could be meaningful in discovering proper muscle stimulation and may contribute to suggest appropriate and effective WBV exercise protocols. ©2009 IEEE.

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Vibration treatment by oscillating platforms is more and more employed in the fields of exercise physiology and bone research. The rationale of this treatment is based on the neuromuscular system response elicited by vibration loads. surface Electromyography (EMG) is largely utilized to assess muscular response elicited by vibrations and Root Mean Square of the electromyography signals is often used as a concise quantitative index of muscle activity; in general, EMG envelope or RMS is expected to increase during vibration. However, it is well known that during surface bio-potential recording, motion artifacts may arise from relative motion between electrodes and skin and between skin layers. Also the only skin stretch, modifying the internal charge distribution, results in a variation of electrode potential. The aim of this study is to highlight the movements of muscles, and the succeeding relevance of motion artifacts on electrodes, in subjects undergoing vibration treatments. EMGs from quadriceps of fifteen subjects were recorded during vibration at different frequencies (15-40 Hz); Triaxial accelerometers were placed onto quadriceps, as close as possible to muscle belly, to monitor motion. The computed muscle belly displacements showed a peculiar behavior reflecting the mechanical properties of the structures involved. Motion artifact related to the impressed vibration have been recognized and related to movement of the soft tissues. In fact large artifacts are visible on EMGs and patellar electrodes recordings during vibration. Signals spectra also revealed sharp peaks corresponding to vibration frequency and its harmonics, in accordance with accelerometers data. © 2008 Springer-Verlag.

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Older adults may have trouble when performing activities of daily living due to decrease in physical strength and degradation of neuromotor and musculoskeletal function. Motor activation patterns during Lateral Step Down and Step Up from 4-inch and 8-inch step heights was assessed in younger (n=8, 24.4 years) and older adults (n=8, 58.9 years) using joint angle kinematics and electromyography of lower extremity muscles. Ground reaction forces were used to ascertain the loading, stabilization and unloading phases of the tasks. Older adults had an altered muscle activation sequence and significantly longer muscle bursts during loading for the tibialis anterior, gastrocnemius, vastus medialis, bicep femoris, gluteus medius and gluteus maximus muscles of the stationary leg. They also demonstrated a significantly larger swing time (579.1 ms vs. 444.8 ms) during the step down task for the moving leg. The novel data suggests presence of age-related differences in motor coordination during lateral stepping.

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Aim : To evaluate and to standardize surface electromyography (sEMG) normalization procedures for respiratory muscles by comparing muscle activation during Maximal Voluntary Isometric Contraction (MVIC) and Maximal Respiratory Pressures (MIP, MEP and sniff test). Methods: Healthy subjects were evalua ted regarding demographics, spirometry and sEMG during the five maneuvers: sniff test, MIP , MEP and Maximal Voluntary Isometric C ontraction (MVIC) of RA, SCM and SC A . For electrode placement, skin was prepared with abrasion, followed by shaving in the foll owing regions for acquisition of el ectromyographic signals: (1) SC M: lower third of the distance between the mastoid process and t he sternoclavicular joint; (2) SC A : 5 cm to the right from the sternoclavicular joint and at this point, up to 2 cm; and (3 ) RA: the level of umbilicus, 4 cm to the right. In electromyographic variables analysis , the data normality was assessed by Shapiro - Wilk test. Comparisons among studied maneuvers were performed by Friedman Test and Dunn’s post - hoc for multiple comparisons a mong inspiratory maneuvers, and Mann Whitney test for expiratory maneuvers. Subgroups differences between genders were performed by Student's t test or Mann - Whitney test according to data normality. Results: 35 subjects participated in the study, b ut 5 we re excluded (BMI> 25 kg/ m²). Sample consisted of 30 subjects (1 5 women), mean age 27.3±7.43 years, BMI 22.2 ± 1.69 kg/m² and spirometric indices within normal limits. Specific MVIC for SCM, SCA and RA showed the highest RMS. When we grouped sample into gender we found no difference among RMS values for the studied SCM maneuvers, while for SCA, MVIC SCM / SCA was the one with the highest RMS and for RA, MVIC RA in men. Once considering women, MVIC SCM/SCA showed the highest RMS for SCM, SCA and MVIC RA showed t he highest value for RA. Conclusion: MVIC for SCM, SCA and RA muscles showed the highest RMS values. When comparing RMS between the studied groups, there was no significant difference between men and women.

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The classifier support vector machine is used in several problems in various areas of knowledge. Basically the method used in this classier is to end the hyperplane that maximizes the distance between the groups, to increase the generalization of the classifier. In this work, we treated some problems of binary classification of data obtained by electroencephalography (EEG) and electromyography (EMG) using Support Vector Machine with some complementary techniques, such as: Principal Component Analysis to identify the active regions of the brain, the periodogram method which is obtained by Fourier analysis to help discriminate between groups and Simple Moving Average to eliminate some of the existing noise in the data. It was developed two functions in the software R, for the realization of training tasks and classification. Also, it was proposed two weights systems and a summarized measure to help on deciding in classification of groups. The application of these techniques, weights and the summarized measure in the classier, showed quite satisfactory results, where the best results were an average rate of 95.31% to visual stimuli data, 100% of correct classification for epilepsy data and rates of 91.22% and 96.89% to object motion data for two subjects.

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Objective: to investigate the immediate effect of the vibrating platform on the neuromuscular performance of the quadriceps femoris and on the postural oscillation of subjects submitted to Anterior Cruciate Ligament (ACL) reconstruction. Materials and methods: this study is a randomized and blind clinical trial. Forty-four male volunteers (average age of 27,4 ±6,2 IMC of 26,85± 3,8 Kg/m² and post surgery timeframe of 17± 1,4 weeks) were randomized into two groups: OFF platform (n=22, protocol of exercise over the vibrating platform off) and ON platform (n=22 protocol of exercise over the vibrating platform on, 50Hz frequency and 4mm of amplitude). All volunteers were submitted to assessment the isokinetic evaluation of the quadriceps femoris (isometric and isokinetic at 60°/s) and of the electromyography activity of the muscles Vasto Lateralis (VL) and Vasto Medialis (VM), besides the postural oscillation (baropodometry) in two distinct moments: before and immediately after the intervention protocol. The data was analyzed through the SPSS 20.0 software, with a 5% significance level. To verify the homogeneity of the groups it was used an ANOVA one way, and a ANOVA mixed model to compare the intra and inter groups. Results: it was observed differences between the pre and the post, to latero lateral velocity, isometric torque peak and total work in comparison with intragroup. However, it wasn’t verified any difference in comparing the intergroup in the preevaluation and in the post-evaluation protocol over the vibrating platform. Conclusion: the use of the vibrating platform doesn’t change as an immediate manner the isokinetic performance of the quadriceps femoris, the electromyography activity of the VL and the VM, also doesn’t interfere with the postural oscillation of individuals that were submitted to the ACL reconstruction.

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Background: The inspiratory muscle training (IMT) has been considered an option in reversing or preventing decrease in respiratory muscle strength, however, little is known about the adaptations of these muscles arising from the training with charge. Objectives: To investigate the effect of IMT on the diaphragmatic muscle strength and function neural and structural adjustment of diaphragm in sedentary young people, compare the effects of low intensity IMT with moderate intensity IMT on the thickness, mobility and electrical activity of diaphragm and in inspiratory muscles strength and establish a protocol for conducting a systematic review to evaluate the effects of respiratory muscle training in children and adults with neuromuscular diseases. Materials and Methods: A randomized, double-blind, parallel-group, controlled trial, sample of 28 healthy, both sexes, and sedentary young people, divided into two groups: 14 in the low load training group (G10%) and 14 in the moderate load training group (G55%). The volunteers performed for 9 weeks a home IMT protocol with POWERbreathe®. The G55% trained with 55% of maximal inspiratory pressure (MIP) and the G10% used a charge of 10% of MIP. The training was conducted in sessions of 30 repetitions, twice a day, six days per week. Every two weeks was evaluated MIP and adjusted the load. Volunteers were submitted by ultrasound, surface electromyography, spirometry and manometer before and after IMT. Data were analyzed by SPSS 20.0. Were performed Student's t-test for paired samples to compare diaphragmatic thickness, MIP and MEP before and after IMT protocol and Wilcoxon to compare the RMS (root mean square) and median frequency (MedF) values also before and after training protocol. They were then performed the Student t test for independent samples to compare mobility and diaphragm thickness, MIP and MEP between two groups and the Mann-Whitney test to compare the RMS and MedF values also between the two groups. Parallel to experimental study, we developed a protocol with support from the Cochrane Collaboration on IMT in people with neuromuscular diseases. Results: There was, in both groups, increased inspiratory muscle strength (P <0.05) and expiratory in G10% (P = 0.009) increase in RMS and thickness of relaxed muscle in G55% (P = 0.005; P = 0.026) and there was no change in the MedF (P> 0.05). The comparison between two groups showed a difference in RMS (P = 0.04) and no difference in diaphragm thickness and diaphragm mobility and respiratory muscle strength. Conclusions: It was identified increased neural activity and diagrammatic structure with consequent increase in respiratory muscle strength after the IMT with moderate load. IMT with load of 10% of MIP cannot be considered as a placebo dose, it increases the inspiratory muscle strength and IMT with moderate intensity is able to enhance the recruitment of muscle fibers of diaphragm and promote their hypertrophy. The protocol for carrying out the systematic review published in The Cochrane Library.

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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.

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A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

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A number of studies in the areas of Biomedical Engineering and Health Sciences have employed machine learning tools to develop methods capable of identifying patterns in different sets of data. Despite its extinction in many countries of the developed world, Hansen’s disease is still a disease that affects a huge part of the population in countries such as India and Brazil. In this context, this research proposes to develop a method that makes it possible to understand in the future how Hansen’s disease affects facial muscles. By using surface electromyography, a system was adapted so as to capture the signals from the largest possible number of facial muscles. We have first looked upon the literature to learn about the way researchers around the globe have been working with diseases that affect the peripheral neural system and how electromyography has acted to contribute to the understanding of these diseases. From these data, a protocol was proposed to collect facial surface electromyographic (sEMG) signals so that these signals presented a high signal to noise ratio. After collecting the signals, we looked for a method that would enable the visualization of this information in a way to make it possible to guarantee that the method used presented satisfactory results. After identifying the method's efficiency, we tried to understand which information could be extracted from the electromyographic signal representing the collected data. Once studies demonstrating which information could contribute to a better understanding of this pathology were not to be found in literature, parameters of amplitude, frequency and entropy were extracted from the signal and a feature selection was made in order to look for the features that better distinguish a healthy individual from a pathological one. After, we tried to identify the classifier that best discriminates distinct individuals from different groups, and also the set of parameters of this classifier that would bring the best outcome. It was identified that the protocol proposed in this study and the adaptation with disposable electrodes available in market proved their effectiveness and capability of being used in different studies whose intention is to collect data from facial electromyography. The feature selection algorithm also showed that not all of the features extracted from the signal are significant for data classification, with some more relevant than others. The classifier Support Vector Machine (SVM) proved itself efficient when the adequate Kernel function was used with the muscle from which information was to be extracted. Each investigated muscle presented different results when the classifier used linear, radial and polynomial kernel functions. Even though we have focused on Hansen’s disease, the method applied here can be used to study facial electromyography in other pathologies.

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The aim of this study was to present a new methodology for evaluating the pelvic floor muscle (PFM) passive properties. The properties were assessed in 13 continent women using an intra-vaginal dynamometric speculum and EMG (to ensure the subjects were relaxed) in four different conditions: (1) forces recorded at minimal aperture (initial passive resistance); (2) passive resistance at maximal aperture; (3) forces and passive elastic stiffness (PES) evaluated during five lengthening and shortening cycles; and (4) percentage loss of resistance after 1 min of sustained stretch. The PFMs and surrounding tissues were stretched, at constant speed, by increasing the vaginal antero-posterior diameter; different apertures were considered. Hysteresis was also calculated. The procedure was deemed acceptable by all participants. The median passive forces recorded ranged from 0.54 N (interquartile range 1.52) for minimal aperture to 8.45 N (interquartile range 7.10) for maximal aperture while the corresponding median PES values were 0.17 N/mm (interquartile range 0.28) and 0.67 N/mm (interquartile range 0.60). Median hysteresis was 17.24 N∗mm (interquartile range 35.60) and the median percentage of force losses was 11.17% (interquartile range 13.33). This original approach to evaluating the PFM passive properties is very promising for providing better insight into the patho-physiology of stress urinary incontinence and pinpointing conservative treatment mechanisms.

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Aims The purpose of this study was to examine the effect of a pelvic floor muscle (PFM) rehabilitation program on incontinence symptoms, PFM function, and morphology in older women with SUI. Methods Women 60 years old and older with at least weekly episodes of SUI were recruited. Participants were evaluated before and after a 12-week group PFM rehabilitation intervention. The evaluations included 3-day bladder diaries, symptom, and quality of life questionnaires, PFM function testing with dynamometry (force) and electromyography (activation) during seven tasks: rest, PFM maximum voluntary contraction (MVC), straining, rapid-repeated PFM contractions, a 60 sec sustained PFM contraction, a single cough and three repeated coughs, and sagittal MRI recorded at rest, during PFM MVCs and during straining to assess PFM morphology. Results Seventeen women (68.9 ± 5.5 years) participated. Following the intervention the frequency of urine leakage decreased and disease-specific quality of life improved significantly. PFM function improved significantly: the participants were able to perform more rapid-repeated PFM contractions; they activated their PFMs sooner when coughing and they were better able to maintain a PFM contraction between repeated coughs. Pelvic organ support improved significantly: the anorectal angle was decreased and the urethrovescial junction was higher at rest, during contraction and while straining. Conclusions This study indicated that improvements in urine leakage were produced along with improvements in PFM co-ordination (demonstrated by the increased number of rapid PFM contractions and the earlier PFM activation when coughing), motor-control, pelvic organ support.

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The aim of this study was to present a new methodology for evaluating the pelvic floor muscle (PFM) passive properties. The properties were assessed in 13 continent women using an intra-vaginal dynamometric speculum and EMG (to ensure the subjects were relaxed) in four different conditions: (1) forces recorded at minimal aperture (initial passive resistance); (2) passive resistance at maximal aperture; (3) forces and passive elastic stiffness (PES) evaluated during five lengthening and shortening cycles; and (4) percentage loss of resistance after 1 min of sustained stretch. The PFMs and surrounding tissues were stretched, at constant speed, by increasing the vaginal antero-posterior diameter; different apertures were considered. Hysteresis was also calculated. The procedure was deemed acceptable by all participants. The median passive forces recorded ranged from 0.54 N (interquartile range 1.52) for minimal aperture to 8.45 N (interquartile range 7.10) for maximal aperture while the corresponding median PES values were 0.17 N/mm (interquartile range 0.28) and 0.67 N/mm (interquartile range 0.60). Median hysteresis was 17.24 N∗mm (interquartile range 35.60) and the median percentage of force losses was 11.17% (interquartile range 13.33). This original approach to evaluating the PFM passive properties is very promising for providing better insight into the patho-physiology of stress urinary incontinence and pinpointing conservative treatment mechanisms.

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Aims The purpose of this study was to examine the effect of a pelvic floor muscle (PFM) rehabilitation program on incontinence symptoms, PFM function, and morphology in older women with SUI. Methods Women 60 years old and older with at least weekly episodes of SUI were recruited. Participants were evaluated before and after a 12-week group PFM rehabilitation intervention. The evaluations included 3-day bladder diaries, symptom, and quality of life questionnaires, PFM function testing with dynamometry (force) and electromyography (activation) during seven tasks: rest, PFM maximum voluntary contraction (MVC), straining, rapid-repeated PFM contractions, a 60 sec sustained PFM contraction, a single cough and three repeated coughs, and sagittal MRI recorded at rest, during PFM MVCs and during straining to assess PFM morphology. Results Seventeen women (68.9 ± 5.5 years) participated. Following the intervention the frequency of urine leakage decreased and disease-specific quality of life improved significantly. PFM function improved significantly: the participants were able to perform more rapid-repeated PFM contractions; they activated their PFMs sooner when coughing and they were better able to maintain a PFM contraction between repeated coughs. Pelvic organ support improved significantly: the anorectal angle was decreased and the urethrovescial junction was higher at rest, during contraction and while straining. Conclusions This study indicated that improvements in urine leakage were produced along with improvements in PFM co-ordination (demonstrated by the increased number of rapid PFM contractions and the earlier PFM activation when coughing), motor-control, pelvic organ support.