Effects of minimum sampling rate and signal reconstruction on surface electromyographic signals

Abstract

Signal oversampling is frequently used to prevent distortion in time-series representations. When sampling at rates just above the Nyquist critical frequency ( f c), Shannon’s reconstruction theorem provides an alternative means of circumventing this problem. The purpose of this study was to determine whether surface electromyographic (EMG) data is compromised when sampled just above f c and whether Shannon’s reconstruction theorem can correct these deficiencies, if present. Brief isometric elbow flexion contractions were performed at 100%, 80%, 50%, 25%, 10%, 5% and 2.5% maximum voluntary contraction (MVC) followed by one trial of random cyclic isometric contractions and relaxations. The 2 kHz signal was resampled at 1 kHz and the 2 and 1 kHz signals were reconstructed (2RS and 1RS, respectively) to a sampling rate of 20 kHz. Data were full-wave rectified (FWR) and low-pass filtered (LE). Peak amplitudes of the FWR and LE signals, average EMG (AEMG) amplitudes of the FWR and LE signals, mean power frequency of the raw data, number of gaps, and mean gap time of the LE signals were calculated. Significant differences were present in peak EMG and AEMG measurements between the 1 and 2 kHz, 2RS and 20 kHz signals and occasionally between the 1RS and the 2 kHz, 2RS and 20 kHz signals. These differences, although statistically significant were quite small amounting to less than 0.5% MVC. No significant differences were found for the gaps parameters. The small differences seen, coupled with the processing time required for signal reconstruction, make oversampling as well as signal reconstruction in surface EMG measurements unnecessary.