1. Muscle Fatigue, Electromyography, and Wavelet Analysis (Now What?)
Joseph P. Weir
Neuromechanics Laboratory
Department of Health, Sport, and Exercise Sciences
University of Kansas

2. Muscle Fatigue, Electromyography, and Wavelet Analysis (Now What
Muscle Fatigue, Electromyography, and Wavelet Analysis (Now What?) (a plea for help)
Joseph P. Weir
Neuromechanics Laboratory
Department of Health, Sport, and Exercise Sciences
University of Kansas

4. What Does EMG Record? EMG systems record muscle action potentials
EMG electrodes record the depolarization and repolarization (action potentials) of muscle cell membranes (the sarcolemma).
For surface EMG, in general the larger the number of active motor units and the higher the firing rate, the larger the voltage changes associated with the contraction, and the larger the EMG amplitude (size of the EMG signal).

5. Surface Electromyography
Used to record "gross" muscle activity, i.e., many motor units contribute to the surface EMG signal. The primary use of surface EMG is to provide information about timing of muscle activity, muscle force production, and muscle fatigue.

7. Netter’s Essential Physiology 2009 conduction velocity
muscle fiber size/type
“The motor unit conduction velocity ranged from 2.6 to 5.3 m/s with a mean of 3.7 m/s.” (note myelinated nerve fiber CV ~ m/sec)
J Physiol October; 391: 561–571.
PMCID: PMC
Muscle fibre conduction velocity in motor units of the human anterior tibial muscle: a new size principle parameter.
S Andreassen and L Arendt-Nielsen

8. analog-to-digital conversion
De Luca, C.J. Electromyography. Encyclopedia of Medical Devices and Instrumentation, (John G. Webster, Ed.) John Wiley Publisher, , 2006.
De Luca, C.J. Electromyography. Encyclopedia of Medical Devices and Instrumentation, (John G. Webster, Ed.) John Wiley Publisher, , 2006.

12. Time Domain Analysis How “big” is the signal? rms amplitude
full wave rectification (absolute value) then integration

13. Frequency Domain Analysis

15. Fast Fourier Transform (FFT)
Discrete Fourier Transform (DFT)

16. Time (msec)
V2/Hz
median power frequency Hz

17. Clark et al JAP 2003

18. Wavelets Fourier analysis assumes stationary data.
Limits it’s use in examining changes in frequency characteristics over time e.g., in response to perturbations like exercise, changes in muscle length, etc.
Joint Time Frequency Analysis
Short-time Fourier analysis (Gabor)
Wigner-Ville
Wavelets

19. Wavelets Wavelet  small wave Idea: Compare wavelet against signal
Dilate and compress wavelet for different segments of signal
Replace frequency with “scale”

21. Wavelets Continuous wavelet transform – computationally intensive
Discrete wavelet transform
Sub-band coding
Sequential low pass and high pass filters (and downsampling).
2n data points

22. signal LP HP Scaling coeff Wavelet coeff LP HP Wavelet coeff
256 – 512 Hz LP HP
Wavelet coeff Hz
Scaling coeff LP HP
Wavelet coeff
64 – 128 Hz
Scaling coeff LP HP
Wavelet coeff
32 – 64 Hz
Scaling coeff

24. Von Tscharner Modification of Cauchy Wavelets

29. Other Biological Signals
Heart Rate Variability
Mechanomyography
EEG

30. .

31. The normal value of HRV should be in the range of 1. 5 – 2
The normal value of HRV should be in the range of 1.5 – Subject A’s is 1.02 which is a little lower than normal, however,this does not show ANS dysfunction

32. Subject B’s HRV ratio was 4. 12 with the normal ratio being between 1
Subject B’s HRV ratio was 4.12 with the normal ratio being between 1.5 and 2.0.
Subject B showed very little High frequency variability in her data.

33. Wavelets
Wavelets give us the potential ability to look at the kinetics of the autonomic response to a perturbation such as exercise and tilt.
HR recovery following GXT is a prognostic indicator in CAD – vagal influence.
Examine autonomic responses to potentially dangerous situations e.g., sudden strenuous exercise, shoveling snow.
Stratify risk in different patient populations.

35. Pattern Recognition (Magic)
“Briefly, the principal components analysis involves decomposition of the intensity patterns onto a set of orthogonal principal components, the maximum possible number of which is equal to the total number of intensity patterns. The result of the decomposition is a set of weights (p_vectors) that quantifies the amount of variability that can be explained by each of the principal components. The p_vectors can then be projected onto Fisher’s Discriminant, which allows them to be visualized on a two dimensional distribution.” (Beck et al 2008)

36. Discrimination Using Pattern Recognition of EMG Intensity Patterns
Males vs. Females during gait (95%)
Sprint vs. Endurance Athletes
Fatigued vs. Non-Fatigued