Abstract
Clinical analysis of the electromyogram is a powerful tool for diagnosis of neuromuscular diseases. There fore, the detection and the analysis of electromyogram signals has he attracted much attention over the years. Several methods based on modern signal Processing techniques such as temporal analysis, spectro-temporel analysis ..., have been investigated for electromyogram signal treatment. However, many of these analysis methods are not highly successful due to their complexity and non-stationarity. The aim of this study is to analyse the EMGs signals using nonlinear analysis. This analysis can provide a wide range of information’s related to the type of signal (normal and pathological).
Highlights
The EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities
We focus on the following three features of the recurrence plot as they best describe the behaviour of the underlying EMG signals: a
The recurrence plots for normal case and Myopathy case are quite distinct compared to Neuropathy case
Summary
The EMG signal is a biomedical signal that measures electrical currents generated in muscles during its contraction representing neuromuscular activities. The nervous system always controls the muscle activity (contraction/relaxation). The EMG signal is a complicated signal, which is controlled by the nervous system and is dependent on the anatomical and physiological properties of muscles. Detection and analysis of EMGs signals with powerful and advance methodologies is becoming a very important requirement in biomedical engineering. Nonlinear tools have been introduced to analyze the EMGs signals; among them, the recurrence quantification analysis (RQA) and the bispectral analysis. Recurrence quantification analysis (RQA) methods for biological signals such as electroencephalogram [2] and EMG [3], are useful since it does not require large data and does not depend on statistical nature of signal [4]
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