Muscle fiber conduction velocity and mean power spectrum frequency in neuromuscular disorders and in fatigue

Abstract

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.

Multiscale entropy-based approach to automated surface EMG classification of neuromuscular disorders

“…Hence, iEMG signals are always relatively sparse and distinction between myopathy and neuropathy is typically based on MUAP shapes [2,16,17]. But also in iEMG there are features that are not capable of making the distinction between neuropathy and myopathy, such as MFCV [11,38] and fibrillation potentials [11].…”

“…The model for simulation of pathological changes in sEMG was introduced in [7]. The most investigated sEMG parameters include MFCV [33,38,39], MU size [9,32], frequency spectra [13,15], sEMG amplitude [9,22] and sEMG entropy [7,14]. However, although some studies have demonstrated feasibility of the detection of neuromuscular diseases from sEMG [9,14,30], the reported classification accuracy typically ranges from 70% to 80%.…”