~
This study investigated the relation of muscle fiber conduction velocity (MFCV) to difference power spectrum mean frequency (MF), their fatigue trends, and differences between their values and their fatigue trends in various neuromuscular disorders. Electromyographic interference pattern was recorded inside the biceps in continuous isometric maximal voluntary contractions. Each subject was encouraged to pull for as long as possible. Fatigue was calculated as percent of time to complete inability to sustain contraction. The MFCV was computed by cross-correlation. The MF was computed by differencing, windowing, FFT, squaring of coefficient, and repeat averaging. There were 33 healthy, 86 polyneuropathic, 28 myasthenic, 13 myotonic, and 32 myopathic patients. Both MFCV and MF changed significantly with fatigue-the MFCV linearly, while the MF in a markedly nonlinear fashion. Both were found to be insensitive to the end stages of muscle fatigue- the MFCV did not change its slope toward complete fatigue, and the MF did not change at all beyond the 40% fatigue point. A statistically sound fatigue regression equation was derived for each, and a nonlinear equation was found to best describe their relationship. Neither MFCV nor its fatigue changes were found to be significantly different across the neuromuscular disorders. The MF, however, was found to be significantly different in some neuromuscular disorders in both its average values and fatigue trends. This study showed, in contrast to the literature, a nonlinear relationship between MFCV and MF. It also shows that neither the MFCV nor the MF had reasonable diagnostic power on its own; however, the MF was very promising to serve as an adjunct to other variables. Key words: EMG interference pattern mean frequency power spectrum * muscle fiber conduction * neuromuscular disorders * fatigue MUSCLE & NERVE 15~780-787 1992
MUSCLE FIBER CONDUCTION VELOCITY AND MEAN POWER SPECTRUM FREQUENCY IN NEUROMUSCULAR DISORDERS AND IN FATIGUE ISRAEL YAAR, MD, and LES NILES, PhD
Muscle fiber conduction velocity (MFCV) is a decisive factor in generating the configuration and am litude of the motor unit action potential r.7.9.12. , other factors are the spread of the innervation zone,92" the number of muscle fibers in the motor unit (MU) and their type,''''* the elec-
'J
From the Neurology Section. VA Medical Center, Providence, Rhode Island (Dr. Yaar); and Xerox Palo Alto Research Center, Palo Alto, California (Dr. Niles) Acknowledgments. We thank Sheryl Chicoine for typing the manuscript. Supported by the VA Merti Review. Address reprint requests to Israel Yaar, MD, Chief, Neurology Section, VA Medical Center, Davis Park (127A). Providence, RI 02908 Presented in abstrct form at the AAEM Meeting in Vancouver, 1991 Accepted for publication November 20, 1991 CCC 0148-639x1921070780-08 $04 00 0 1992 John Wiley & Sons, Inc
780
Muscle Fiber Conduction and Mean Frequency
trodes used," the electrical properties of the volume conductor,12 and more. As the EMG interference pattern is generated by the temporal and spatial summation of motor unit action potentials it is also affected by these factors. MFCV'L75z'' and additional factors may change iri fatigue and, as a result, have special effects on the EMG interference pattern, e.g., motor unit synchronization,3,4,12,13,16 changes in the motor unit firing rates 2,3,5-7,12.15 the recruitment of large motor units," and more. T h e motor unit action potentials and the EMG interference patterns are very useful in the diagnosis of neuromuscular disorders. Therefore, it has been suggested in the literature that MFCV should be a good criterion in the diagnosis of neuromuscular disorders.2,8,'",'2,'~ There has not been much research into the possibility that fatigue changes of the MFCV may be of
MUSCLE & NERVE
July 1992
help in the diagnosis of neuromuscular disorders. Also, it has been stated that the EMG power spectrum mean fre uenc (MF) is linearly dependent on the MFCV, ',12 that it is a good indicator of muscular f a t i g ~ e , ~ * ~and ~ " ~that ' * it can be used in the diagnosis of neuromuscular disorders.2,8.10- 12
9, ,Y I
1
T o investigate some of these assumptions, published data, and conclusions, the present work explored the basic relationship between MFCV and MF, their values and fatigue trends in healthy and disease muscles, and last, their diagnostic power.
I
7 i t
0
10
20
E: \MRNUS\CFITGI .ENG
30
50
40
70
60
80
100
90
% FRTIGUE
MATERIAL AND METHODS
Electromyographic interference patterns were recorded from biceps of 192 subjects. There were 108 men and 84 women, 8 to 80 years of age (43 ? 15 years); 33 were normal, 86 neuropathic, 28 myasthenic, 13 myotonic, and 32 myopathic.
Subjects.
Recording. The recordings were done from two locations inside the biceps muscles, as per our previously published t e ~ h n i q u e . ' Each ~ subject was encouraged to exert maximum continuous contraction for as long as possible. Data Reduction and Variables Generation. T h e two EMG interference pattern signals, s,(t) and s,(t), were analyzed in repeating 5.84 long segments with 60% overlap. s,(t) Was subtracted from
s d t ) for each epoch. Two hundred fifty-six (256)
data points of this difference signal were Hanning-windowed at a time; FFTed; frequency coefficients squared, added, and averaged 38 1 times to produce a power spectrum. The mean frequency of the power spectrum (MF) was calculated by dividing each power spectrum order-one moment by its order-zero moment.I2 T h e MFCV was computed for each epoch by cross-correlating s,(t) with s,(t) .33 RESULTS AND STATISTICAL ANALYSES MFCV Data Set
1. All cases' MFCV values were plotted against fatigue as a percent of the time that the subject was able to pull in a scatterplot of 3323 data points. The best polynomial fit was found in stepwise multiple regression analysis to be a straight line (not shown here). 2. For every case with four or more valid MFCV values both before and after 50% fatigue was reached, a separate regression was performed, resulting in: FO = calculated MFCV at 0% fa-
Muscle Fiber Conduction and Mean Frequency
FIGURE 1. MFCV changes with fatigue by diagnostic group.
tigue, B = slope, and F l O O = MFCV at complete fatigue. 3. Using FO and F l O O of each subject, group regression was calculated to each of the diagnostic groups (see Fig. 1 and Table 1). Statistics were calcdated by MANOVA and by nonparametric sign test, and show significant slowing of MFCV with fatigue for the normal, the neuopathy, and the myasthenia gravis groups. The changes of MFCV with fatigue in myotonia and myopathy groups are not significant. 4. Repeated measure MANOVA analysis was applied to all cases with valid FO and F l O O values in full factorial design adjusting for age as a covariate. The resulting P values are shown in Table 2. In summary, overall highly significant slowing of MFCV with fatigue was found. There were no significant differences between the groups in average MFCV or in its changes with fatigue. Age, sex, and their interaction effects were not significant. Pairwise comparisons of the five groups (not shown here) failed to show significant differences. Table 1. MFCV and MF values at no fatigue (FO),and the significance of the fatigue trends. MF
MFCV
Fatigue trend,
Fatigue trend, Group
Fo*
PC
FOt
P
This study investigated the relation of muscle fiber conduction velocity (MFCV) to difference power spectrum mean frequency (MF), their fatigue trends, and differences between their values and their fatigue trends in various neuromuscular disorders. Electromyographic interference pattern was recorded inside the biceps in continuous isometric maximal voluntary contractions. Each subject was encouraged to pull for as long as possible. Fatigue was calculated as percent of time to complete inability to sustain contraction. The MFCV was computed by cross-correlation. The MF was computed by differencing, windowing, FFT, squaring of coefficient, and repeat averaging. There were 33 healthy, 86 polyneuropathic, 28 myasthenic, 13 myotonic, and 32 myopathic patients. Both MFCV and MF changed significantly with fatigue-the MFCV linearly, while the MF in a markedly nonlinear fashion. Both were found to be insensitive to the end stages of muscle fatigue- the MFCV did not change its slope toward complete fatigue, and the MF did not change at all beyond the 40% fatigue point. A statistically sound fatigue regression equation was derived for each, and a nonlinear equation was found to best describe their relationship. Neither MFCV nor its fatigue changes were found to be significantly different across the neuromuscular disorders. The MF, however, was found to be significantly different in some neuromuscular disorders in both its average values and fatigue trends. This study showed, in contrast to the literature, a nonlinear relationship between MFCV and MF. It also shows that neither the MFCV nor the MF had reasonable diagnostic power on its own; however, the MF was very promising to serve as an adjunct to other variables. Key words: EMG interference pattern mean frequency power spectrum * muscle fiber conduction * neuromuscular disorders * fatigue MUSCLE & NERVE 15~780-787 1992
MUSCLE FIBER CONDUCTION VELOCITY AND MEAN POWER SPECTRUM FREQUENCY IN NEUROMUSCULAR DISORDERS AND IN FATIGUE ISRAEL YAAR, MD, and LES NILES, PhD
Muscle fiber conduction velocity (MFCV) is a decisive factor in generating the configuration and am litude of the motor unit action potential r.7.9.12. , other factors are the spread of the innervation zone,92" the number of muscle fibers in the motor unit (MU) and their type,''''* the elec-
'J
From the Neurology Section. VA Medical Center, Providence, Rhode Island (Dr. Yaar); and Xerox Palo Alto Research Center, Palo Alto, California (Dr. Niles) Acknowledgments. We thank Sheryl Chicoine for typing the manuscript. Supported by the VA Merti Review. Address reprint requests to Israel Yaar, MD, Chief, Neurology Section, VA Medical Center, Davis Park (127A). Providence, RI 02908 Presented in abstrct form at the AAEM Meeting in Vancouver, 1991 Accepted for publication November 20, 1991 CCC 0148-639x1921070780-08 $04 00 0 1992 John Wiley & Sons, Inc
780
Muscle Fiber Conduction and Mean Frequency
trodes used," the electrical properties of the volume conductor,12 and more. As the EMG interference pattern is generated by the temporal and spatial summation of motor unit action potentials it is also affected by these factors. MFCV'L75z'' and additional factors may change iri fatigue and, as a result, have special effects on the EMG interference pattern, e.g., motor unit synchronization,3,4,12,13,16 changes in the motor unit firing rates 2,3,5-7,12.15 the recruitment of large motor units," and more. T h e motor unit action potentials and the EMG interference patterns are very useful in the diagnosis of neuromuscular disorders. Therefore, it has been suggested in the literature that MFCV should be a good criterion in the diagnosis of neuromuscular disorders.2,8,'",'2,'~ There has not been much research into the possibility that fatigue changes of the MFCV may be of
MUSCLE & NERVE
July 1992
help in the diagnosis of neuromuscular disorders. Also, it has been stated that the EMG power spectrum mean fre uenc (MF) is linearly dependent on the MFCV, ',12 that it is a good indicator of muscular f a t i g ~ e , ~ * ~and ~ " ~that ' * it can be used in the diagnosis of neuromuscular disorders.2,8.10- 12
9, ,Y I
1
T o investigate some of these assumptions, published data, and conclusions, the present work explored the basic relationship between MFCV and MF, their values and fatigue trends in healthy and disease muscles, and last, their diagnostic power.
I
7 i t
0
10
20
E: \MRNUS\CFITGI .ENG
30
50
40
70
60
80
100
90
% FRTIGUE
MATERIAL AND METHODS
Electromyographic interference patterns were recorded from biceps of 192 subjects. There were 108 men and 84 women, 8 to 80 years of age (43 ? 15 years); 33 were normal, 86 neuropathic, 28 myasthenic, 13 myotonic, and 32 myopathic.
Subjects.
Recording. The recordings were done from two locations inside the biceps muscles, as per our previously published t e ~ h n i q u e . ' Each ~ subject was encouraged to exert maximum continuous contraction for as long as possible. Data Reduction and Variables Generation. T h e two EMG interference pattern signals, s,(t) and s,(t), were analyzed in repeating 5.84 long segments with 60% overlap. s,(t) Was subtracted from
s d t ) for each epoch. Two hundred fifty-six (256)
data points of this difference signal were Hanning-windowed at a time; FFTed; frequency coefficients squared, added, and averaged 38 1 times to produce a power spectrum. The mean frequency of the power spectrum (MF) was calculated by dividing each power spectrum order-one moment by its order-zero moment.I2 T h e MFCV was computed for each epoch by cross-correlating s,(t) with s,(t) .33 RESULTS AND STATISTICAL ANALYSES MFCV Data Set
1. All cases' MFCV values were plotted against fatigue as a percent of the time that the subject was able to pull in a scatterplot of 3323 data points. The best polynomial fit was found in stepwise multiple regression analysis to be a straight line (not shown here). 2. For every case with four or more valid MFCV values both before and after 50% fatigue was reached, a separate regression was performed, resulting in: FO = calculated MFCV at 0% fa-
Muscle Fiber Conduction and Mean Frequency
FIGURE 1. MFCV changes with fatigue by diagnostic group.
tigue, B = slope, and F l O O = MFCV at complete fatigue. 3. Using FO and F l O O of each subject, group regression was calculated to each of the diagnostic groups (see Fig. 1 and Table 1). Statistics were calcdated by MANOVA and by nonparametric sign test, and show significant slowing of MFCV with fatigue for the normal, the neuopathy, and the myasthenia gravis groups. The changes of MFCV with fatigue in myotonia and myopathy groups are not significant. 4. Repeated measure MANOVA analysis was applied to all cases with valid FO and F l O O values in full factorial design adjusting for age as a covariate. The resulting P values are shown in Table 2. In summary, overall highly significant slowing of MFCV with fatigue was found. There were no significant differences between the groups in average MFCV or in its changes with fatigue. Age, sex, and their interaction effects were not significant. Pairwise comparisons of the five groups (not shown here) failed to show significant differences. Table 1. MFCV and MF values at no fatigue (FO),and the significance of the fatigue trends. MF
MFCV
Fatigue trend,
Fatigue trend, Group
Fo*
PC
FOt
P
