A model computation of how synchronization and clustering of motor unit action potentials alter the power spectra of electromyograms. (January 2019)

• Record Type: Journal Article
• Authors: von Tscharner, Vinzenz
• Is Part Of: Biomedical signal processing and control. Volume 47(2019)
• Page Start: 344
• Page End: 349
• Publication Date: 2019-01
• Subjects: Clustering of motor unit action potentials -- Motor unit synchronization -- EMG signal cancelling -- EMG power spectra -- Bursts of muscle activity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.09.002
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Clustering of motor unit action potentials results in a modulation of the power spectra of non-clustered ones. Clustered motor unit action potentials result in higher EMG power at low frequencies. The mean frequency is altered by clustered motor unit action potentials. The modulation can be separated from the actual power spectrum of the motor unit action potential. Abstract: Introduction: The duration of bursts of muscle activity e.g. while running or walking, is too short for long pulse trains of motor unit action potentials (MUAPs) to develop. A pool of motor units is likely activated simultaneously which generates clustered MUAPs. The hypothesis is that the EMG power spectra are modulated by the fact that MUAPs cluster. The purpose is to quantify this modulation analytically. Methods: A model of an EMG signal is presented that includes clustered MUAPs. Results: According to the model the influence of MUAPs clustering is shown to be largest at lower frequencies and increases when the width of the time window containing the clusters decreases. Discussion: The power at frequencies below 60 Hz strongly reflects changes of the degree of clustering. The mean frequency of the EMG therefore decreases when MUAPs cluster more tightly. Thus, clustering of MUAPs competes with other physiological properties that influence the mean frequency. The EMG power is proportional to the number of active MUAPs at high frequencies but approaches a value proportional to the square of the number of active MUAPs at very low frequencies. To obtain a measure of amplitude that is proportional to the number of active motor units, one should focus on the higher frequency power components only, however, to monitor the effect of clustering of MUAP one should focus on the lower frequency power. That could become relevant for comparing pre- and post-operative clinical gait studies where changes in MUAPs clustering may play a significant role.