Muscle Fatigue during Dynamic Contractions Assessed by New Spectral Indices, 2006, Dimitrov et al.

PURPOSE
The aim of the present study was to test the applicability and sensitivity of new electromyography (EMG) spectral indices in assessing peripheral muscle fatigue during dynamic knee-extension exercise.

METHODS
Seven subjects completed 10 sets of 15 repetitions of right knee–extension exercise lifting 50% of their one-repetition maximum. Torque (T), knee-joint angle, and the interference EMG of rectus femoris muscle were recorded simultaneously. Maximal voluntary isometric contraction (MVC) was tested before and after exercise. Median spectral frequency (Fmed ) and new spectral indices of muscle fatigue (FInsmk ) were calculated for each repetition.

RESULTS
The rate and range of FInsmk - and Fmed -relative changes against the first repetition of the corresponding set increased gradually across successive repetitions within the set, reflecting accumulation of peripheral muscle fatigue. The maximal change of FInsmk observed in the present experiment was approximately eightfold, whereas that of F med was only 32%. Significant between-subject variability in the range of FI nsmk changes (P G 0.0001) was found, so a hierarchical cluster analysis of muscle fatigue indices was conducted. Three distinct subgroups of subjects were identified: high (N = 1, FI nsmk change 9 400%), medium (N = 4, 200% G FInsmk change G 400%), and low (N = 2, FI nsmk change G 200%) muscle fatigability. The changes in muscle performance during (last vs first repetition peak T, P = 0.03) and after (post- vs preexercise MVC, P = 0.012) exercise were significantly different between clusters (one-way ANOVA). The rate of fatigue development was also significantly different between clusters (linear regression analysis of Fmed and FI nsmk changes).

CONCLUSIONS
The new spectral indices are a valid and reliable tool for assessment of muscle fatigability irrespective of EMG signal variability caused by dynamic muscle contractions, and these indices are more sensitive than those traditionally used.

Link (Medicine & Science in Sports & Exercise)

 

@Hutan posted from it:
Further studies are needed to fully establish the validity and reliability of the new spectral indices for testing muscle performance in the clinical, rehabilitation, and sports setting.

So, a tiny study, not validated, least of all for comparing ill vs healthy.

Wallit uses this (and an overlapping set of data from 8 pwme vs 6 hv) to claim that:
"a relative decrease in the slope of the Dimitrov index17,18 (Fig. 4b) occurred in PI-ME/CFS participants but both remained constant in HVs, suggesting that the decline of force was not due to peripheral fatigue or a neuromuscular disorder."

Why is so much science half-baked?

 

So, if I understand correctly muscle fatigue manifests itself on electromyography (EMG) by an increase in amplitude and a shift towards lower frequencies. A bit like a cyclist tends to use a bigger gear and fewer resolutions when he is fatigued.

Ratios between high and low frequency ranges have been adopted as an indication of peripheral muscle fatigue but unfortunately, the selection boundaries are mostly subjective. The authors write: ‘The establishment of objective criteria for selection of border frequency or frequency bands is problematic because the spectral power–density distribution depends not only on the development of muscle fatigue but also on the muscle fiber-to-electrode distances, electrode type, electrode longitudinal position, muscle fiber length, and volume conductor properties. These factors will vary between subjects and experimental set-ups.’

Therefore, some have tried to use relative measures such as Fmean and Fmed which are calculated using EMG power spectral density function. Dimitrov and colleagues write that these methods are not sensitive enough. Therefore, they have developed a new measure which they call FInsmk that is based on higher-order derivatives of the density function. They think this is better because it showed a larger change in the measurements taken before, during and after knee exercise in 7 healthy individuals.

The graphs shown in figure 2 indicate that the ‘relative change of FInsmk (%)’ goes up as the number of repetitions (and thus fatigue?) increases. The mathematical calculation is quite complex making it difficult to interpret this measure but it seems that it calculates a ratio of low versus high frequencies. When it goes up, the low frequencies have increased, signalling muscle fatigue.

 

This study tested the Dimitrov Index: "The log FInsm5 as a single parameter predictor accounted for 37% of the performance variance of changes in muscle power" but this was in 15 healthy participants who did 10 leg presses with 2 minutes in between.
EMG spectral indices and muscle power fatigue during dynamic contractions - ScienceDirect

 

I wonder whether they could measure the nerve signals via non-contact means. Place an antenna near the leg, in a shielded room to avoid EMF noise, and process the signals. Direct measurement was needed in the early days of electronics, but now we have digital processing for separating signals and presenting them in a useful way.

 

This recent Japanese study also used the Dimitrov index, although they call it the spectral fatigue index (SFI). They tested 15 collegiate male athletes underwent three nonexplosive back squat tasks comprising low, medium, and high volumes. There was a significant relationship between Rate of Perceived Exertion (RPE) and and SFI (r=0.573, p<0.001) but SFI was not good at differentiating the different volumes. The authors write:

Validity of using perceived exertion to assess muscle fatigue during back squat exercise | BMC Sports Science, Medicine and Rehabilitation | Full Text (biomedcentral.com)