Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions.

Many algorithms have been described in the literature for estimating amplitude, frequency variables and conduction velocity of the surface EMG signal detected during voluntary contractions. They have been used in different application areas for the non invasive assessment of muscle functions. Although many studies have focused on the comparison of different methods for information extraction from surface EMG signals, they have been carried out under different conditions and a complete comparison is not available. It is the purpose of this paper to briefly review the most frequently used algorithms for EMG variable estimation, compare them using computer generated as well as real signals and outline the advantages and drawbacks of each. In particular the paper focuses on the issue of EMG amplitude estimation with and without pre-whitening of the signal, mean and median frequency estimation with periodogram and autoregressive based algorithms both in stationary and non-stationary conditions, delay estimation for the calculation of muscle fiber conduction velocity.

[1]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[2]  J Duchêne,et al.  Surface electromyogram during voluntary contraction: processing tools and relation to physiological events. , 1993, Critical reviews in biomedical engineering.

[3]  R. Merletti,et al.  Effect of FFT based algorithms on estimation of myoelectric signal spectral parameters , 1989, Images of the Twenty-First Century. Proceedings of the Annual International Engineering in Medicine and Biology Society,.

[4]  Neville Hogan,et al.  Myoelectric Signal Processing: Optimal Estimation Applied to Electromyography - Part II: Experimental Demonstration of Optimal Myoprocessor Performance , 1980, IEEE Transactions on Biomedical Engineering.

[5]  Shihab S Asfour,et al.  Effects of time windowing on the estimated EMG parameters , 1996 .

[6]  T A Wilson,et al.  Model for a pump that drives circulation of pleural fluid. , 1995, Journal of applied physiology.

[7]  Dario Farina,et al.  A novel approach for precise simulation of the EMG signal detected by surface electrodes , 2001, IEEE Trans. Biomed. Eng..

[8]  N Ishii,et al.  Stationarity and normality test for biomedical data. , 1977, Computer programs in biomedicine.

[9]  D. Stegeman,et al.  Variability and interrelationships of surface EMG parameters during local muscle fatigue , 1993, Muscle & nerve.

[10]  R. Silberstein,et al.  Factors determining the frequency content of the electromyogram. , 1983, Journal of applied physiology: respiratory, environmental and exercise physiology.

[11]  J. Rissanen.  A UNIVERSAL PRIOR FOR INTEGERS AND ESTIMATION BY MINIMUM DESCRIPTION LENGTH , 1983 .

[12]  Kevin C. McGill,et al.  High-Resolution Alignment of Sampled Waveforms , 1984, IEEE Transactions on Biomedical Engineering.

[13]  E. Parzen.  Some recent advances in time series modeling , 1974 .

[14]  Paolo Bonato,et al.  Comparison between muscle fiber conduction velocity estimation techniques: spectral matching versus crosscorrelation , 1990 .

[15]  Carlo J. De Luca,et al.  A Note on the Noninvasive Estimation of Muscle Fiber Conduction Velocity , 1985, IEEE Transactions on Biomedical Engineering.

[16]  Serge H. Roy,et al.  Modeling of surface myoelectric signals. II. Model-based signal interpretation , 1999, IEEE Transactions on Biomedical Engineering.

[17]  R. Merletti,et al.  Surface EMG signal processing during isometric contractions. , 1997, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[18]  C L Webber,et al.  Influence of isometric loading on biceps EMG dynamics as assessed by linear and nonlinear tools. , 1995, Journal of applied physiology.

[19]  Knaflitz,et al.  Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. , 1990, Journal of applied physiology.

[20]  R. Scott,et al.  A Nonstationary Model for the Electromyogram , 1977, IEEE Transactions on Biomedical Engineering.

[21]  Jun Yu,et al.  Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study , 2000, IEEE Transactions on Biomedical Engineering.

[22]  S. R. Smith,et al.  High-resolution alignment of action potential waveforms using cubic spline interpolation. , 1988, Journal of biomedical engineering.

[23]  R. Merletti,et al.  Modeling of surface myoelectric signals. I. Model implementation , 1999, IEEE Transactions on Biomedical Engineering.

[24]  E. Clancy,et al.  Influence of smoothing window length on electromyogram amplitude estimates , 1998, IEEE Transactions on Biomedical Engineering.

[25]  G. Hagg,et al.  Interpretation of EMG spectral alterations and alteration indexes at sustained contraction. , 1992 .

[26]  N. Hogan,et al.  Probability density of the surface electromyogram and its relation to amplitude detectors , 1999, IEEE Transactions on Biomedical Engineering.

[27]  C. D. De Luca,et al.  Frequency Parameters of the Myoelectric Signal as a Measure of Muscle Conduction Velocity , 1981, IEEE Transactions on Biomedical Engineering.

[28]  G.C. Deangelis,et al.  Standardized evaluation of techniques for measuring the spectral compression of the myoelectric signal , 1990, IEEE Transactions on Biomedical Engineering.

[29]  H. Hermens,et al.  European recommendations for surface electromyography: Results of the SENIAM Project , 1999 .

[30]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[31]  H. Akaike.  A new look at the statistical model identification , 1974 .

[32]  Hervé Rix,et al.  Detecting Small Variations in Shape , 1980 .

[33]  A Goswami,et al.  Effect of sampling frequency on EMG power spectral characteristics. , 1994, Electromyography and clinical neurophysiology.

[34]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.

[35]  M Knaflitz,et al.  Time-frequency methods applied to muscle fatigue assessment during dynamic contractions. , 1999, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[36]  G. Inbar,et al.  Autoregressive Modeling of Surface EMG and Its Spectrum with Application to Fatigue , 1987, IEEE Transactions on Biomedical Engineering.

[37]  E.A. Clancy,et al.  Electromyogram amplitude estimation with adaptive smoothing window length , 1999, IEEE Transactions on Biomedical Engineering.

[38]  C. J. Luca.  Myoelectrical manifestations of localized muscular fatigue in humans. , 1984 .

[39]  N. Hogan,et al.  Single site electromyograph amplitude estimation , 1994, IEEE Transactions on Biomedical Engineering.

[40]  R. Merletti,et al.  Comparison between myoelectric signal mean and median frequency estimates , 1988, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  C. D. De Luca,et al.  Effects of muscle fiber type and size on EMG median frequency and conduction velocity. , 1995, Journal of applied physiology.