Electromyography Signal Analysis and Characterization: Based on Wavelet Transform and Higher Order Statistics - Couverture souple

Hussain, Sazzad; Reaz, Mamun Bin Ibne

 
9783848412501: Electromyography Signal Analysis and Characterization: Based on Wavelet Transform and Higher Order Statistics
Couverture souple
ISBN 10 : 3848412500 ISBN 13 : 9783848412501
Editeur : LAP LAMBERT Academic Publishing, 2012

Synopsis

Electromyography (EMG) signal gives an electrical representation of neuromuscular activation associated with contracting muscle provides information about the performance of muscles and nerves. EMG signal acquires noise while traveling through different tissues. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interference noise. Higher Order Statistics (HOS) can suppress white Gaussian noise in detection, parameter estimation and solve classification problems. Based on the RMS error, it is noticed that WF db2 can perform denoising most effectively among the other WFs (db6, db8, dmey). Power spectrum analysis is performed to the denoised EMG where mean power frequency is calculated to indicate changes in muscle contraction. Gaussianity and linearity tests are conducted to understand changes in muscle contraction. According to the results, increase in muscle contraction provides significant increase in EMG mean power frequency. The study also verifies that the power spectrum of EMG shows a shift to lower frequencies during fatigue. The bispectrum analysis shows that the signal becomes less Gaussian and more linear with increasing muscle force.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Electromyography (EMG) signal gives an electrical representation of neuromuscular activation associated with contracting muscle provides information about the performance of muscles and nerves. EMG signal acquires noise while traveling through different tissues. With the appropriate choice of the Wavelet Function (WF), it is possible to remove interference noise. Higher Order Statistics (HOS) can suppress white Gaussian noise in detection, parameter estimation and solve classification problems. Based on the RMS error, it is noticed that WF db2 can perform denoising most effectively among the other WFs (db6, db8, dmey). Power spectrum analysis is performed to the denoised EMG where mean power frequency is calculated to indicate changes in muscle contraction. Gaussianity and linearity tests are conducted to understand changes in muscle contraction. According to the results, increase in muscle contraction provides significant increase in EMG mean power frequency. The study also verifies that the power spectrum of EMG shows a shift to lower frequencies during fatigue. The bispectrum analysis shows that the signal becomes less Gaussian and more linear with increasing muscle force.

Biographie de l'auteur

Sazzad Hussain was born in Bangladesh, in November 1983. He is a Ph.D. candidate with the Learning and Affect Technologies Engineering (LATTE) Research Group at the University of Sydney, Australia. His research focus is on Multimodal Affect & Cognitive Load Detection, Machine Learning, Information Fusion and Physiological Information Processing.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2012
Langue
anglais
ISBN 10 
3848412500
ISBN 13 
9783848412501
Reliure
Broché
Nombre de pages
132
Coordonnées du fabricant
non disponible
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