پوشش سیگنال الکترونیکی الکترومیوگرافی
Abstract
1- Introduction
2- Mathematical model
3- Methodology
4- Results
5- Conclusions
Acknowledgements
References
Abstract
In this work, we introduce two novel methodologies to compute the envelope of superficial electromyography signals. Our methods are based on the detection of activation and deactivation patterns using a change-point approach on the variances of the sample. More concretely, an iterative algorithms is proposed to select the change-points between two segments of the signal based on some local statistics introduced in this work. The signal is split up into two segments, and a new search for change-points is recursively conducted in each subsequence. The change-points make possible to calculate local envelopes which reflect the shape of the signal without ignoring the activation and deactivation landmarks. Two methods are proposed in this work, and the improvements with respect to methodologies available in the literature are shown using both synthetic and real data. A thorough analysis of the techniques is performed to that end.
Introduction
Electromyography (EMG) signals are an important topic of active research in view of their wide range of medical applications. For example, EMG signals have been classified using different criteria in order to diagnose neuromuscular disorders [1], they have been used as a tool in the evaluation of generalized tonic–clonic seizure semiology [2], in the recognition of emotions using facial recordings and statistical methods [3], in the automatic control of upper limbprosthesis [4], as a criterionto determine the differences in lower-extremity muscular activation walking between older and young adults [5], in the investigation of neck and shoulder muscle activity of orthodontists in natural environments [6] and as a mechanism to measure shoulder muscle fatigue during repetitive tasks [7] among other interesting biomedical applications. It is important to recall that EMG signals are measurements of the difference of electric potentials between two electrodes. In turn, these measurements are highly correlated to the intensity in muscular activity [8]. There are various well-established procedures to record EMG signals, one of them is called superficial electromyography (sEMG), which consists in placing the electrodes over the skin covering the muscle of interest using a conductive gel to get better data readings [9]. An sEMG signal is essentially a random stationary temporal series in which the activity of the measured muscle is reflected as an increase in the signal amplitude (also called a ‘burst’ in this work). Muscular activity may be identified by finding the localization, duration, shape and amplitude of those bursts in the electric signal.