Relation between EMG power spectrum shifts and muscle fibre action potential conduction velocity changes during local muscular fatigue in man
Muscular fatigue was studied in the human m. biceps brachii by contracting the muscle as long as possible at 50% of maximum voluntary strength. For 8 subjects changes in the muscle fibre action potential conduction velocity could successfully be measured using the cross-correlation technique between two surface EMG signals. Shifts in the EMG power spectrum were quantified using the mean power frequency (MPF) of the EMG power spectral density function.
During fatigue the EMG power spectrum gradually shifted to lower frequencies. The mean value and standard deviation of the MPF value decreased from (115±20) Hz at the beginning of the experiment to (60±18) Hz at the end. For 4 of the 8 subjects the decrease in MPF value was accompanied by a substantial decrease in conduction velocity (mean decrease was 33% of the initial velocity value). For the other 4 subjects, despite the great MPF changes, there was hardly any change in conduction velocity (mean decrease was 3%).
The present study shows that great EMG power spectral shifts during muscular fatigue may occur without a concomitant change in muscle fibre action potential conduction velocity.
Key wordsMuscular fatigue Isometric contraction M. biceps brachii EMG power spectrum shift Muscle fibre action potential conduction velocity
Unable to display preview. Download preview PDF.
- Basmaijan JV (1980) Electromyography — Dynamic gross anatomy: a review. Am J Anat 159: 245–260Google Scholar
- Bendat JS, Piersol AG (1971) Random data: analysis and measurement procedures. Wiley — Interscience, New YorkGoogle Scholar
- Bigland-Ritchie B, Donovan EF, Roussos CS (1981) Conduction velocity and EMG power spectrum changes in fatigue of sustained maximal efforts. J Appl Physiol-Respirat Environ 51: 1300–1305Google Scholar
- Blank A, Gonen B, Masora A (1979) The size of active motor units in the initiation and maintenance of an isometric contraction carried out to fatigue. Electromyogr Clin Neurophysiol 19: 535–539Google Scholar
- Blinowska K, Piotrkiewicz M (1978) A study of surface electromyograms by the means of digital simulation. II. Spectral analysis of simulated and experimental electromyograms. Electromyogr Clin Neurophysiol 18: 95–105Google Scholar
- Buchthal F, Guld C, Rosenfalck P (1955) Innervation zone and progagation velocity in human muscle. Acta Physiol Scand 35: 174–190Google Scholar
- Clamann HP (1970) Activity of single motor units during isometric tension. Neurology 20: 254–260Google Scholar
- De Luca CJ (1979) Physiology and mathematics of myoelectric signals. IEEE Trans Biomed Engineer 26: 313–325Google Scholar
- Frisk-Holmberg M, Jörfeldt L, Juhlin-Dannfelt A, Karlsson J (1981) Leg blood flow during excercise in man in relation to muscle fibre composition. Acta Physiol Scand 112: 339–342Google Scholar
- Gydikov A, Dimitrova N, Kosarov D, Dimitrov G (1976) Influence of frequency and duration of firing on the shape of potentials from different types of motor units in human muscles. Exp Neurol 52: 345–355Google Scholar
- HÄgg G (1981) Electromyographic fatigue analysis based on the number of zero crossings. Pflügers Arch Physiol 391: 78–80Google Scholar
- Kwatny E, Thomas D, Kwatny HG (1970) An application of signal processing techniques to the study of myoelectric signals. IEEE Trans Biomed Engineer 17: 303–313.Google Scholar
- LindstrØm L, Magnusson R, Petersen I (1970) Muscular fatigue and action potential conduction velocity changes studied with frequency analysis of EMG signals. Electromyography 4: 341–356Google Scholar
- LindstrØm L, Kadefors R, Petersen I (1977) An electromyographic index for localized muscle fatigue. J Appl Physiol: Respirat Environ Exerc Physiol 43: 750–755Google Scholar
- Lippold OCJ, Redfearn JWT, Vuco J (1960) The electromyography of fatigue. Ergonomics 3: 121–131Google Scholar
- Maton B (1981) Human motor activity during the onset of muscle fatigue in submaximal isometric isotonic contraction. Eur J Appl Physiol 46: 271–281Google Scholar
- Naeije M, Zorn H (1981) Changes in the power spectrum of the surface electromyogram of the human masseter muscle due to local muscular fatigue. Arch Oral Biol 26: 409–412Google Scholar
- Naeije M, Zorn H (1982) Estimation of the action potential conduction velocity in human skeletal muscle using the surface EMG cross-correlation technique. Electromyogr Clin Neurophysiol (in press)Google Scholar
- Papoulis A (1973) Minimum-bias windows for high resolution spectral estimates. IEEE Trans Informat Theory 19: 9–12Google Scholar
- Person RS, Mishin LN (1964) Auto- and cross-correlation analysis of the electrical activity of muscles. Med Electron Biol Engineer 2: 155–159Google Scholar
- Person RS, Kudina LP (1968) Cross-correlation of electromyogram showing interference pattern. Electroenceph Clin Neurophysiol 25: 58–68Google Scholar
- Person RS, Libkind MS (1970) Simulation of electromyograms showing interference patterns. Electroenceph Clin Neurophysiol 28: 625–632Google Scholar
- Stålberg E (1966) Propagation velocity in human muscle fibres in situ. Acta Physiol Scand 287: 3–112Google Scholar
- Stulen FB, De Luca CJ (1981) Frequency parameters of the myoelectric signal as a measure of muscle conduction velocity. IEEE Trans Biomed Engineer 28: 515–523Google Scholar
- Viitasalo JHT, Komi PV (1977) Signal characteristics of EMG during fatigue. Eur J Appl Physiol 37: 111–121Google Scholar