Analysis of Muscle Fatigue Progression in Biceps Brachii Using Surface Electromyography Signals and Wavelet Packet Entropies
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1
Research Scholar, Biomedical Engineering Group, Department of Applied Mechanics, India
Muscle fatigue is a neuromuscular condition where muscles fail to generate the expected or required force to perform the task continuously. It is associated with both central and peripheral nervous systems. This condition can also be caused due to neuromuscular disorders such as Parkinson’s disease, Carcinoma, Facioscapulohumeral dystrophy, Myotonic dystrophy and Hereditary motor and sensory neuropathy type I.
Surface electromyography (sEMG) is a non-invasive technique, which records the electrical activity of neuromuscular system. It is a complex, nonstationary and multicomponent signal. Most of the real life activities such as cyclic exercise and walking are based on dynamic contraction of muscles. Degree of nonstationarity is increased in dynamic contractions due to recruitment and de-recruitment of motor units, movement of innervations zones with respect to electrode and changes in muscle fiber length, firing rate and conduction velocity. The sEMG signals are often analyzed in the time domain, frequency domain and time-frequency domain. Although several methods of signal analyses are reported in the literature, the analysis of fatigue progression still remains a challenging task.
In this work, an attempt has been made to analyze progression of fatigue in biceps brachii muscle using surface EMG signals and wavelet packet entropies. The sEMG signals are recorded from biceps brachii of fifty healthy volunteers under well defined protocol and are preprocessed. The preprocessed signals are divided into six equal epochs. Further, these signals are subjected to wavelet packet transform. The entropies such as Shannon, threshold, norm, sure and log energy are extracted from the wavelet packet coefficients. The results show that Shannon, threshold and norm entropies are found to be distinct in all zones. Also the seperability of these entropies are appreciable between zone 1 and zone 6 which corresponds to non-fatigue and fatigue zone respectively. The t-test performed gives a p-value less than 0.0001 implying that the features are extremely significant. The demographics of subjects, representative sEMG signals and threshold entropy for all zones are shown below.
Keywords:
biceps brachii,
muscle fatigue progression,
surface EMG,
Wavelet packet entropy,
threshold entropy,
shannan entropy,
norm entropy
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Poster, not to be considered for oral presentation
Topic:
Electrophysiology
Citation:
P A
K and
S
R
(2014). Analysis of Muscle Fatigue Progression in Biceps Brachii Using Surface Electromyography Signals and Wavelet Packet Entropies.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00048
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Received:
27 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Mr. Karthick P A, Research Scholar, Biomedical Engineering Group, Department of Applied Mechanics, Chennai, Tamil nadu, India, Tamilnadu, 600 036, India, pakarthick1@gmail.com