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

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

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).

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.

http://mobility.egloos.com/2447589 http://mobility.egloos.com/2447589

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 ~ 80-120 m/sec) J Physiol. 1987 October; 391: 561–571. PMCID: PMC1192232 Muscle fibre conduction velocity in motor units of the human anterior tibial muscle: a new size principle parameter. S Andreassen and L Arendt-Nielsen

analog-to-digital conversion De Luca, C.J. Electromyography. Encyclopedia of Medical Devices and Instrumentation, (John G. Webster, Ed.) John Wiley Publisher, 98-109, 2006. De Luca, C.J. Electromyography. Encyclopedia of Medical Devices and Instrumentation, (John G. Webster, Ed.) John Wiley Publisher, 98-109, 2006.

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

Frequency Domain Analysis

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

Time (msec) V2/Hz median power frequency Hz

Clark et al JAP 2003

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

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

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

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

Von Tscharner Modification of Cauchy Wavelets

Other Biological Signals Heart Rate Variability Mechanomyography EEG

.

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 – 2.0. Subject A’s is 1.02 which is a little lower than normal, however,this does not show ANS dysfunction

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.

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.

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)

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