Download PDF
Methods Inf Med 1997; 36(04/05): 302-305
DOI: 10.1055/s-0038-1636867
Original Article
Schattauer GmbH

Multivariate Analysis of Multichannel Surface Myoelectric Signals to Determine Muscular Fatigue

Y. Nakamura
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
,
T. Kiryu
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
,
Y. Saitoh
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
1   Graduate School of Science and Technology, Niigata University, Niigata, Japan
› Author Affiliations
Further Information

Publication History

Publication Date:
19 February 2018 (online)

    Permissions and Reprints

Abstract:

Multivariate analysis would be effective in finding the functional change from several variables obtained from multivariate biological signals. We applied this idea to the discrimination of sustained fatiguing contraction from negative ramp contraction. The time-series of eigenvalues were obtained from multidimensional biological variables by the Karhunen-Loève expansion. The results showed that, the first and second eigenvalues came close to each other during fatiguing contraction, whereas only the first eigenvalue was dominant during negative ramp contraction. Moreover, the factor loadings showed considerable difference between fatiguing contraction and negative ramp contraction. As a result, the muscular-fatigue-related functional change could be represented clearly by the time-series of proportions and factor loadings.

Keywords:

Muscular Fatigue - Karhunen-Loève Expansion - Eigenvalue - Factor Loading
 

  • REFERENCES

  • 1 Kugler J. ECG classification based on Karhunen-Loève expansion and discriminant analysis. Proc 6th Int Conf on Pattern Recognition. 1982: 623-5.
    PubMedGoogle Scholar
  • 2 Childers DG, Bloom PA, Arroyo AA, Roucos SE, Fischler IS, Achariyapaopan T, Perry Jr NW. Classification of cortical response using features from single EEG records. IEEE Trans Biomed Eng BME 1982; 29: 423-8.
    PubMedGoogle Scholar
  • 3 Kiryu T, Okada M, Ssaitoh Y. Change of the relationship between superimposed M wave and preceding background activity during muscular fatigue. Proc 16th Annu Int Conf IEEE/EMBS 1994; 01: 339-40.
    PubMedGoogle Scholar
  • 4 Tesch P, Sjdin B, Thorstensson A, Karlsson J. Muscle fatigue and its relation to lactate accumulation and LDH activity in man. Acta Physiol Scand 1970; 219: 1324-9.
    PubMedGoogle Scholar
  • 5 De Luca CJ. Myoelectrical manifestations of localized muscular fatigue in humans. CRC Crit Rev Bioeng 1984; 11: 251-79.
    PubMedGoogle Scholar
  • 6 Merletti R, Lo Conte LP, Orizio C. Indices of muscle fatigue. J Electromyogr Kinesiol 1991; 01: 20-33.
    CrossrefPubMedGoogle Scholar
  • 7 Erkki O. Subspace Methods of Pattern Recognition. Research Studies Press. 1986
    PubMedGoogle Scholar
  • 8 Naeije M, Zorn H. Estimation of the action potential conduction velocity in human skeletal muscles using the surface EMG crosscorrelation technique. Electromyogr Clin Neurophysiol 1983; 23: 73-80.
    PubMedGoogle Scholar
  • 9 Kaneko H, Kiryu T, Ssaitoh Y. Compensation for the distortion of bipolar surface EMG signals caused by innervation zone movement. IEICE Trans Inf & Syst E79-D 1996; 04: 373-81.
    PubMedGoogle Scholar
  • 10 Triolo RJ, Lawrence M. An automated method for describing muscle fatigue. Proc 16th Annual Int Conf IEEE Eng Med Bio Soc 1994; 02: 337-8.
    PubMedGoogle Scholar