The EMG Signal

EMG Frequency Spectrum

Fatigue

Signal Processing.4

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) 70 - 125 Hz

– Fast twitch motor units (phasic - Type II)» Frequency range = 126 - 250 Hz

The Power Spectrum

ST FT

ST = Slow twitch mu’sFT = Fast twitch mu’s

Bandwidth

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

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)

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)

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

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)

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

Muscle Fatigue.7

Therefore a “spectral shift to the left”

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

Frequency-Domain Analysis.1

Transformation from the time domain to the frequency domain– Fast Fourier Transformation (FFT)

» Fourier series of equations

Frequency-Domain Analysis.2

Removes the time between successive action potentials so that they appear as periodic functions of time

Pre-fatigue

Fatigue

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)

Fatigue

Pre-fatigue

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

graph

Frequency-Domain Analysis.5

Major factors that cause an active change in frequency– Action potential shape (see above)– Decrease motor unit discharge rate

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)

Frequency-Domain Analysis.7

Outcome: a decrease in median power frequency

Shift to the left

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

Median Power Frequency Calculation Procedure

Sample data in multiples of x2 (Example 1024 Hz)

Median Power Frequency Calculation Procedure

Sample data in multiples of x2 (Example 1024 Hz)

Rectify and filter (BP or LP) raw signal

Median Power Frequency Calculation Procedure

Sample data at multiples of x2 (Example 1024 Hz)

Rectify and filter (BP or LP) raw signal

Apply FFTHz

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

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

Reference SourcesBass, 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, 86-95.

Reference SourcesDe Luca, C. J. (1984). Myoelectric manifestations of localized

muscular fatigue in humans. CRC critical reviews in biomedical engineering, 11, 251-279.

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, 175-186.

Enoka, R. M. (1994). Neuromechanical basis of kinesiology (Ed. 2). Champaign, Ill: Human Kinetics, pp. 166-170.

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. (www.ni.com) (pn: 340479-01)

Gniewek, M.T. (19xx). Waveform analysis using the Fourier transform. Application Note-11, Great Britain: AT/MCA CODAS-Keithly Instruments, Ltd., pp1-6.

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. (www.ni.com) (pn: 340555-01.

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, 359-370.

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, 260-265.

Merleti, R., Knaflitz, M., & De Luca, C.J. (1990). Myoelectric manifestations of fatigue in voluntary and electrically elicited contractions. Journal of Applied Physiology, 69, 1810-1820.

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, 485-498.

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.

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, 387-392

Winter, D.A. (1990). Biomechanics and motor control of human movement (2nd Ed). New York: John Wiley &

Sons, Inc., 191-212.