Performance of three electromyogram decomposition algorithms as a function of signal to noise ratio: Assessment with experimental and simulated data

We have previously published a full report [25] comparing the performance of three automated electromyogram (EMG) decomposition algorithms. In our prior report, the primary measure of decomposition difficulty/challenge for each data record was the “Decomposability Index” of Florestal et al. [3]. This conference paper is intended to augment our prior work by providing companion results when the measure of difficulty is the motor unit signal-to-noise ratio (SNR_MU) - a measure that is commonly used in the literature. Thus, we analyzed experimental and simulated data to assess the agreement and accuracy, as a function of SNR_MU, of three publicly available decomposition algorithms-EMGlab[1] (single channel data only), Fuzzy Expert [2] and Montreal [3]. Data consisted of quadrifilar needle EMGs from the tibialis anterior of 12 subjects at 10%, 20% and 50% maximum voluntary contraction (MVC); single channel needle EMGs from the biceps brachii of 10 control subjects during contractions just above threshold; and matched simulated data. Performance vs. SNR_MU was assessed via agreement between pairs of algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For experimental data, RMS errors between the achieved agreement and those predicted by an exponential model as a function of SNR_MU ranged from 8.4% to 19.2%. For the simulations, RMS errors between achieved accuracy and those predicted by the SNR_MU exponential model ranged from 3.7% to 14.7%. Agreement/accuracy was strongly related to SNR_MU.