The EMG SignalEMG Frequency SpectrumFatigueSignal Processing.4

Motor Unit Firing RatesFiring rate = frequencyNo. of cycles (firings) per unit of timeExample: 175 cps = 175 Hertz (Hz)Range of frequencies = the (Power) Spectrum = the BandwidthSlow twitch motor units (tonic - Type I)Frequency range = (20) 70 - 125 HzFast twitch motor units (phasic - Type II)Frequency range = 126 - 250 Hz

The Power SpectrumST FTST = Slow twitch musFT = Fast twitch musBandwidth

Muscle Fatigue.1Grossly manifests as a decrease in tension/force (and power) productionInsufficient O2Energy stores used up/exhaustedLactic acid builds upCirculatory system has difficulty removing lactic acidAccumulates in extracellular fluid surrounding muscle fibers (Bass & Moore, 1973; Tasaki et al., 1967)Decreases pH

Muscle Fatigue.2Decreased pH causes a decrease in the conduction velocity of muscle fibersFast 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.3Sustained 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.4With decreased conduction velocity of muscle fibersDecrease in peak twitch tensionsIncrease in contraction timesCorresponding decrease in firing frequencyThe result is a decrease in force

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

Muscle Fatigue.6As fatigue progresses there is a shift to lower frequenciesFast twitch (higher frequency) motor units drop out firstSlow twitch (lower frequency) motor units retained

Muscle Fatigue.7Therefore a spectral shift to the left

Spectral AnalysisIndicies of frequency shift (Soderberg & Knutson, 2000)Mean power frequencyMedian power frequencyMore commonly usedNot 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.1Transformation from the time domain to the frequency domainFast Fourier Transformation (FFT)Fourier series of equations

Frequency-Domain Analysis.2Removes the time between successive action potentials so that they appear as periodic functions of timePre-fatigueFatigue

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

FatiguePre-fatigue

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

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

Frequency-Domain Analysis.6Action potential shapeChanges due to conduction velocity rate along sarcolema of muscle fiberAs 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 rateCauses grouping of action potentiasl at low frequencies ~10 Hz (Krogh-Lund & Jogensen, 1992)

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

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

Median Power Frequency Calculation ProcedureSample data in multiples of x2 (Example 1024 Hz)

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

Median Power Frequency Calculation ProcedureSample data at multiples of x2 (Example 1024 Hz)Rectify and filter (BP or LP) raw signalApply FFT

Hz

Median Power Frequency Calculation ProcedureSample data at multiples of x2 (Example 1024 Hz)Rectify and filter (BP or LP) raw signalApply FFTCompute 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 SourcesFahy, K., Prez, 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 SourcesHarvey, 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 SourcesMoritani, 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 SourcesRamirez, 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 SourcesWeir, 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.