Simplified Optimal Estimation of Time-Varying Electromyogram Standard Deviation (EMGσ): Evaluation on Two Datasets

To facilitate the broader use of EMG signal whitening, we studied four whitening procedures of various complexities, as well as the roles of sampling rate and noise correction. We separately analyzed force-varying and constant-force contractions from 64 subjects who completed constant-posture tasks about the elbow over a range of forces from 0% to 50% maximum voluntary contraction (MVC). From the constant-force tasks, we found that noise correction via the root difference of squares (RDS) method consistently reduced EMG recording noise, often by a factor of 5–10. All other primary results were from the force-varying contractions. Sampling at 4096 Hz provided small and statistically significant improvements over sampling at 2048 Hz (~3%), which, in turn, provided small improvements over sampling at 1024 Hz (~4%). In comparing equivalent processing variants at a sampling rate of 4096 Hz, whitening filters calibrated to the EMG spectrum of each subject generally performed best (4.74% MVC EMG-force error), followed by one universal whitening filter for all subjects (4.83% MVC error), followed by a high-pass filter whitening method (4.89% MVC error) and then a first difference whitening filter (4.91% MVC error)—but none of these statistically differed. Each did significantly improve from EMG-force error without whitening (5.55% MVC). The first difference is an excellent whitening option over this range of contraction forces since no calibration or algorithm decisions are required.

Evaluation of Muscular Strain and Fatigue in Occupational Field Studies Using Long Term Emg Recordings

In occupational field studies surface electromyography can be applied to determine muscular strain and to indicate the occurrence of muscular fatigue. Muscle strain is commonly indicated by using the amplitude of the electromyogram (EMG) or measures thereof, muscle strain is indicated by determining the time course of the EMG with respect to changes in the EMG amplitude or power spectral distribution. For both purposes a detailed knowledge on the current activity of the person under test is needed for the total time under investigation. This can be attained by using a special coding system in which an electrical action code is recorded together with the EMG signals. For the indication of muscular fatigue it has to be considered that the EMG amplitude as well as the EMG spectrum depend not only upon the fatigue state but also upon the produced muscle force. Therefore EMGs should only be compared for situations connected with similar muscle load. This demand can be fulfilled (i) by the execution of test contractions of known force in a defined posture, (ii) by comparing the EMG during situations connected with a similar activity or (iii) in a recently developed approach by the joint analysis of EMG amplitude and spectrum (JASA).

Spectral analysis of human inspiratory diaphragmatic electromyograms

The inspiratory diaphragmatic EMG was recorded via esophageal electrodes in six normal subjects. The EMG and ECG signals were analyzed by power density spectral analysis, before and after band-pass filtering (20--1,600 Hz). The EMG spectrum was concentrated in the bandwidth 25--250 Hz. Electrode motion introduced a significant artifact only at low frequencies. The ECG spectrum was also concentrated at lower frequencies, but substantial power from the cardiac signal spilled over across most of the EMG spectrum. Band-pass filtering was therefore effective in minimizing the former but not the latter. Of the various power and frequency parameters used to quantitate the EMG spectrum, the most stable was the centroid frequency. This was reproducible within and between subjects, and was not affected by changing tidal volume or inspiratory flow rate.