1. EMG Frequency Spectrum Fatigue Signal Processing.4
The EMG Signal
EMG Frequency Spectrum
Fatigue
Signal Processing.4

2. Motor Unit Firing Rates
Firing rate = frequency
No. of cycles (firings) per unit of time
Example: 175 cps = 175 Hertz (Hz)
Range of frequencies = the (Power) Spectrum = the Bandwidth
Slow twitch motor units (tonic - Type I)
Frequency range = (20) Hz
Fast twitch motor units (phasic - Type II)
Frequency range = Hz

3. The Power Spectrum ST = Slow twitch mu’s FT = Fast twitch mu’s
ST FT
Bandwidth

4. Muscle Fatigue.1
Grossly manifests as a decrease in tension/force (and power) production
Insufficient O2
Energy stores used up/exhausted
Lactic acid builds up
Circulatory system has difficulty removing lactic acid
Accumulates in extracellular fluid surrounding muscle fibers (Bass & Moore, 1973; Tasaki et al., 1967)
Decreases pH

5. Muscle Fatigue.2
Decreased pH causes a decrease in the conduction velocity of muscle fibers
Fast twitch (phasic) motor units relying on anaerobic respiration will be more sensitive to circulatory inefficiency and will decrease their activity or stop functioning before slow twitch (tonic - aerobic) motor units (De Luca et al., 1986)

6. Muscle Fatigue.3
Sustained muscle contractions (i.e., isometric) may cause local occlusion of arterioles due to internal pressure and have a similar limiting effect on circulation with resultant decrease in extracellular pH (De Luca et al., 1986)

7. Muscle Fatigue.4 With decreased conduction velocity of muscle fibers
Decrease in peak twitch tensions
Increase in contraction times
Corresponding decrease in firing frequency
The result is a decrease in force

8. Muscle Fatigue.5
With fatigue there is a change in the shape of action potentials (Enoka, 1994)
Decreased amplitude
Increased duration
Result is a EMG spectrum shift to lower frequencies (Winter, 1990)

9. Muscle Fatigue.6
As fatigue progresses there is a shift to lower frequencies
Fast twitch (higher frequency) motor units drop out first
Slow twitch (lower frequency) motor units retained

10. Muscle Fatigue.7
Therefore a “spectral shift to the left”

11. Spectral Analysis
Indicies of frequency shift (Soderberg & Knutson, 2000)
Mean power frequency
Median power frequency
More commonly used
Not susceptible to extremes in the range (bandwidth)
Therefore a more sensitive measure (Knaflitz & De Luca, 1990)
Therefore a decrease in the median power frequency serves as an index of fatigue

12. Frequency-Domain Analysis.1
Transformation from the time domain to the frequency domain
Fast Fourier Transformation (FFT)
Fourier series of equations

13. Frequency-Domain Analysis.2
Removes the time between successive action potentials so that they appear as periodic functions of time
Pre-fatigue
Fatigue

14. Frequency-Domain Analysis.3
Action potentials represented by a best-fitting combination of sine-cosine functions to characterize the frequency and amplitude of the signal
Result is a single line (per frequency)
Pre-fatigue
Fatigue

15. Frequency-Domain Analysis.4
Result is plotted on a frequency-amplitude graph

16. Frequency-Domain Analysis.5
Major factors that cause an active change in frequency
Action potential shape (see above)
Decrease motor unit discharge rate

17. Frequency-Domain Analysis.6
Action potential shape
Changes due to conduction velocity rate along sarcolema of muscle fiber
As conduction velocity decreases the duration of action potential decreases causing a decrease in the median power frequency (De Luca, 1984)
Decrease in motor unit discharge rate
Causes grouping of action potentiasl at low frequencies ~10 Hz (Krogh-Lund & Jogensen, 1992)

18. Frequency-Domain Analysis.7
Outcome: a decrease in median power frequency
Shift to the left

19. Frequency-Domain Analysis.8
Converse relationship with increasing force production
Moritani & Muro (1987) found a significant increase in mean power frequency with increasing force during an MVC of the biceps brachii

20. Median Power Frequency Calculation Procedure
Sample data in multiples of x2 (Example 1024 Hz)

21. Median Power Frequency Calculation Procedure
Sample data in multiples of x2 (Example 1024 Hz)
Rectify and filter (BP or LP) raw signal

22. Median Power Frequency Calculation Procedure
Sample data at multiples of x2 (Example 1024 Hz)
Rectify and filter (BP or LP) raw signal
Apply FFT Hz

23. Median Power Frequency Calculation Procedure
Sample data at multiples of x2 (Example 1024 Hz)
Rectify and filter (BP or LP) raw signal
Apply FFT
Compute median (or mean power) frequency

24. Spec_rev with cursors.vi (with BP filter: cutoffs = 20 & 500 Hz)

25. Reference Sources
Bass, L., & Moore, W.J. (1973). The role of protons in nerve conduction. Progressive Biophysics and Molecular Biology, 27, 143.
Bracewell, R.N. (1989). The Fourier transform. Scientific American, June,

26. Reference Sources
De Luca, C. J. (1984). Myoelectric manifestations of localized muscular fatigue in humans. CRC critical reviews in biomedical engineering, 11,
De Luca, C.J., Sabbahi, M.A., Stulen, F.B., & Bilotto, G. (1982). Some properties of median nerve frequency of the myoelectric signal during localized muscular fatigue. Proceedings of the 5th International Symposium on Biochemistry and Exercise,
Enoka, R. M. (1994). Neuromechanical basis of kinesiology (Ed. 2). Champaign, Ill: Human Kinetics, pp

27. Reference Sources
Fahy, K., Pérez, E. (1993). Fast Fourier transforms and the power spectra in LabVIEW. Application Note 040, February, Austin TX: National Instruments Corp. ( (pn: )
Gniewek, M.T. (19xx). Waveform analysis using the Fourier transform. Application Note-11, Great Britain: AT/MCA CODAS-Keithly Instruments, Ltd., pp1-6.

28. Reference Sources
Harvey, A.F., & Cerna, M. (1993). The fundamentals of FFT-based signal analysis and measurements in LabVIEW and LabWindows. Application Note 041, November, Austin, TX: National Instruments Corp. ( (pn:
Krogh-Lund, C., & Jorgensen, K. (1992). Modification of myo-electric power spectrum in fatgiue from 15% maximal voluntary contraction of human elbow flexor muscles, to limit of endurance: reflection of conduction velocity variation and/or centrally mediated mechanisms? European Journal of Applied Physiology, 64,

29. Reference Sources
Moritani, T., & Muro, M. (1987). Motor unit activity and surface electromyogram power spectrum during increasing force of contraction. European Journal of Applied Physiology, 56,
Merleti, R., Knaflitz, M., & De Luca, C.J. (1990). Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. Journal of Applied Physiology, 69,

30. Reference Sources
Ramirez, R.W. (1985). The FFT: fundamentals and concepts. Englewood Cliffs, NJ: Prentice Hall PTR.
Soderberg, G.L., Knutson, L.M. (2000). A guide for use and interpretation of kinesiologic electromyographic data. Physical Therapy, 80,
Tasaki, I., Singer, I., & Takenaka, T. (1967). Effects of internal and external ionic environment on the excitability of squid giant axon. Journal of General Physiology, 48, 1095.

31. Reference Sources
Weir, J.P., McDonough, A.L., & Hill, V. (1996). The effects of joint angle on electromyographic indices of fatigue. European Journal of Applied Physiology and Occupational Physiology, 73,
Winter, D.A. (1990). Biomechanics and motor control of human movement (2nd Ed). New York: John Wiley & Sons, Inc.,