Local Band Spectral Entropy Based on Wavelet Packet Applied to Surface EMG Signals Analysis

An efficient analytical method for electromyogram (EMG) signals is of great significance to research the inherent mechanism of a motor-control system. In this paper, we proposed an improved approach named wavelet-packet-based local band spectral entropy (WP-LBSE) by introducing the concept of frequency band local-energy (ELF) into the wavelet packet entropy, in order to characterize the time-varying complexity of the EMG signals in the local frequency band. The EMG data were recorded from the biceps brachii (BB) muscle and triceps brachii (TB) muscle at 40°, 100° and 180° of elbow flexion by 10 healthy participants. Significant differences existed among any pair of the three patterns (p < 0.05). The WP-LBSE values of the EMG signals in BB muscle and TB muscle demonstrated a decreased tendency from 40° to 180° of elbow flexion, while the distributions of spectral energy were decreased to a stable state as time periods progressed under the same pattern. The result of this present work is helpful to describe the time-varying complexity characteristics of the EMG signals under different joint angles, and is meaningful to research the dynamic variation of the activated motor units and muscle fibers in the motor-control system.

[1]  P. A. Parker,et al.  Improving myoelectric signal classification using wavelet packets and principal components analysis , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[2]  Zhizhong Wang,et al.  Classification of surface EMG signal using relative wavelet packet energy , 2005, Comput. Methods Programs Biomed..

[3]  Cory M. Smith,et al.  Individual Responses for Muscle Activation, Repetitions, and Volume during Three Sets to Failure of High- (80% 1RM) versus Low-Load (30% 1RM) Forearm Flexion Resistance Exercise , 2015 .

[4]  R. Hughes,et al.  Electromyography-Controlled Exoskeletal Upper-Limb–Powered Orthosis for Exercise Training After Stroke , 2007, American journal of physical medicine & rehabilitation.

[5]  K.-C. Wang,et al.  Robust endpoint detection algorithm based on the adaptive band-partitioning spectral entropy in adverse environments , 2005, IEEE Transactions on Speech and Audio Processing.

[6]  Mehmet Rahmi Canal,et al.  Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals , 2010, Journal of Medical Systems.

[7]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[8]  Ganesh R. Naik,et al.  Nonnegative Matrix Factorization for the Identification of EMG Finger Movements: Evaluation Using Matrix Analysis , 2015, IEEE Journal of Biomedical and Health Informatics.

[9]  Huosheng Hu,et al.  Myoelectric control systems - A survey , 2007, Biomed. Signal Process. Control..

[10]  R. Granit.  The functional role of the muscle spindles--facts and hypotheses. , 1975, Brain : a journal of neurology.

[11]  R. C. Oldfield.  The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[12]  Anas N. Al-Rabadi,et al.  A comparison of modified reconstructability analysis and Ashenhurst‐Curtis decomposition of Boolean functions , 2004 .

[13]  Xu Zhang,et al.  Characterizing the complexity of spontaneous motor unit patterns of amyotrophic lateral sclerosis using approximate entropy , 2011, Journal of neural engineering.

[14]  Marimuthu Palaniswami,et al.  Signal processing evaluation of myoelectric sensor placement in low‐level gestures: sensitivity analysis using independent component analysis , 2014, Expert Syst. J. Knowl. Eng..

[15]  Dennis G. Howe.  Signal-To-Noise Ratio (SNR) For Reliable Data Recording , 1987, Optics & Photonics.

[16]  I. Bialynicki-Birula,et al.  Uncertainty relations for information entropy in wave mechanics , 1975 .

[17]  Nitin Afzulpurkar,et al.  Use of supervised discretization with PCA in wavelet packet transformation-based surface electromyogram classification , 2009, Biomed. Signal Process. Control..

[18]  Xiao Hu,et al.  Identification of surface EMG signals using wavelet packet entropy , 2006 .

[19]  Z Xu,et al.  Digital filter design for peak detection of surface EMG. , 2000, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[20]  Ping Zhou,et al.  Multiscale Entropy Analysis of Different Spontaneous Motor Unit Discharge Patterns , 2013, IEEE Journal of Biomedical and Health Informatics.

[21]  Xin He,et al.  Coordinated alpha and gamma control of muscles and spindles in movement and posture , 2015, Front. Comput. Neurosci..

[22]  I. Jolliffe.  Principal Component Analysis , 2002 .

[23]  Gary G. Yen,et al.  Wavelet packet feature extraction for vibration monitoring , 2000, IEEE Trans. Ind. Electron..

[24]  P. Geethanjali.  Comparative study of PCA in classification of multichannel EMG signals , 2015, Australasian Physical & Engineering Sciences in Medicine.

[25]  D. Farina,et al.  Effect of joint angle on EMG variables in leg and thigh muscles , 2001, IEEE Engineering in Medicine and Biology Magazine.

[26]  Kevin B. Englehart,et al.  A robust, real-time control scheme for multifunction myoelectric control , 2003, IEEE Transactions on Biomedical Engineering.

[27]  Xiaoli Li,et al.  EEG entropy measures in anesthesia , 2015, Front. Comput. Neurosci..

[28]  A. Prochazka,et al.  Sensory systems in the control of movement. , 2012, Comprehensive Physiology.

[29]  P. Matthews.  MUSCLE SPINDLES AND THEIR MOTOR CONTROL. , 1964, Physiological reviews.

[30]  M. Goldfarb,et al.  A Method for the Control of Multigrasp Myoelectric Prosthetic Hands , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[31]  Lucia Rita Quitadamo,et al.  Optimization of EMG-based hand gesture recognition: Supervised vs. unsupervised data preprocessing on healthy subjects and transradial amputees , 2014, Biomed. Signal Process. Control..

[32]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[33]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[34]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[35]  M. Akay,et al.  Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods , 1999, IEEE Transactions on Biomedical Engineering.

[36]  David A Gabriel,et al.  Flexor carpi radialis surface electromyography electrode placement for evoked and voluntary measures , 2015, Muscle & nerve.

[37]  Paul Geladi,et al.  Principal Component Analysis , 1987, Comprehensive Chemometrics.

[38]  Steven M. Pincus.  Approximate entropy as a measure of irregularity for psychiatric serial metrics. , 2006, Bipolar disorders.

[39]  Paavo V. Komi,et al.  EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man , 1979, European Journal of Applied Physiology and Occupational Physiology.

[40]  Jie Liu,et al.  Feature dimensionality reduction for myoelectric pattern recognition: a comparison study of feature selection and feature projection methods. , 2014, Medical engineering & physics.

[41]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[42]  Philippe Faure,et al.  A new method to estimate the Kolmogorov entropy from recurrence plots: its application to neuronal signals , 1998 .

[43]  Xin He,et al.  Fusimotor control of spindle sensitivity regulates central and peripheral coding of joint angles , 2012, Front. Comput. Neurosci..

[44]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[45]  N. Hogan,et al.  Customized interactive robotic treatment for stroke: EMG-triggered therapy , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  B. LeBaron,et al.  A test for independence based on the correlation dimension , 1996 .

[47]  H. Sebastian Seung,et al.  Algorithms for Non-negative Matrix Factorization , 2000, NIPS.

[48]  Zhi-zhong Wang,et al.  Characterization of surface EMG signals using improved approximate entropy , 2006, Journal of Zhejiang University SCIENCE B.

[49]  Ganesh R. Naik,et al.  Identification of Hand and Finger Movements Using Multi Run ICA of Surface Electromyogram , 2012, Journal of Medical Systems.

[50]  K. Englehart,et al.  Classification of the myoelectric signal using time-frequency based representations. , 1999, Medical engineering & physics.

[51]  B. Hannaford,et al.  Short Time Fourier Analysis of the Electromyogram: Fast Movements and Constant Contraction , 1986, IEEE Transactions on Biomedical Engineering.

[52]  M. Bilodeau,et al.  EMG frequency content changes with increasing force and during fatigue in the quadriceps femoris muscle of men and women. , 2003, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[53]  Ganesh R. Naik,et al.  Dependence Independence Measure for Posterior and Anterior EMG Sensors Used in Simple and Complex Finger Flexion Movements: Evaluation Using SDICA , 2015, IEEE Journal of Biomedical and Health Informatics.

[54]  T. Fukuda,et al.  Root Mean Square Value of the Electromyographic Signal in the Isometric Torque of the Quadriceps, Hamstrings and Brachial Biceps Muscles in Female Subjects , 2010 .

[55]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.