Knowledge of electromyography (EMG) among Sub-saharan African patients - a pilot survey

Background: Electromyography (EMG) is one of the common diagnostic procedure in neurology but still scarce in sub-Saharan Africa. Objective: This cross-sectional study evaluated the knowledge of EMG among patients undergoing this procedure, considering the type, quality and reliability of the information they have. Methodology: Consecutive patients who underwent EMG for the first time between 2014 and 2016, at the WFNR/Blossom Medical Centre Ibadan, Nigeria were interviewed prior to their test. Data on patient's demography, type of referring physicians were collated. Knowledge of EMG was also assessed. A patient was considered "informed" if she/he knew, at least, that the EMG is a test that uses an electric current or a needle, that it may be painful or cause discomfort, and that it is used to study the function of muscles and nerves. Descriptive and inferential statistics were performed to know the determinants of EMG knowledge. Result: 55 patients. 32 males and 23 females (mean ages 48.69± 18.32 and 43.30±14.88 respectively) were interviewed. Twenty-three (41.8%) patients were adjudged informed about the nature of EMG while 32 (58.2%) were uninformed about the procedure. Twenty one (38.2%) were informed about the procedure by their doctors while 4 (7.3%) got information from friends and relatives and 4 (7.3%) from the internet. Level of education was associated with being informed (p=0.039) Multivariate logistic regression analysis revealed no significant predictor or EMG knowledge. Conclusion: Knowledge of EMG is poor and could be improved upon. More patient education needs to be done to prepare the patients, and allay their fears about the procedure

Detection of muscle fatigue, from statistical methods to software applications

Background: Muscle fatigue has become increasingly present in our daily lives. This is related to lifestyle difficulties. Several methods were proposed in order to detect the muscle fatigue. This report proposes a short review of statistical methods and processing tools extracted from MATLAB software dedicated to detection of fatigue. Methods: The first part in this study is an application of a useful electronic card named "arduino" for acquiring the electromyography signal (EMG). This latter is the perfect signal to describe fatigue in muscles. The acquisition of data is done in two steps; the first is a simple acquisition representing rest (the subject is relaxed). Then, in the next step, the subject does a series of physical exercises representing moving continuously a handlebar (in order to simulate the work of the tram's conductor). After obtaining raw data from the acquired signals, we apply statistical methods and some processing tools in order to detect fatigue.Results: For the statistical method, we apply the spectral density, which is a mathematical tool that represents the various spectral components of a signal and to perform the harmonic analysis. We deduct that 80 microvolt's is the intensity of getting fatigue (exercise of moving the handlebar). Using processing tools(FFT and STFT techniques), we obtain essential information on the fatigue's beginning time.Conclusion: A brief survey of statistical and processing tools to extract fatigue information from an EMG signal was done. A typical example of the importance of detecting fatigue was also illustrated (tram conductor). We aimed for a lot of results from this study, especially because we want to compare techniques. After studying, the STFT technique seem the best