2 resultados para Emg Signals

em Universidade Federal do Rio Grande do Norte(UFRN)


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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.