Cross-Comparison between Two Multi-channel EMG Decomposition Algorithms Assessed with Experimental and Simulated Data

Abstract

The reliability of automated electromyogram (EMG) decomposition algorithms is important in clinical and scientific studies. In this paper, we analyzed the performance of two multi-channel decomposition algorithms -- Montreal and Fuzzy Expert using both experimental and simulated data. Comparison data consisted of quadrifiler needle EMG from the tibialis anterior muscle of 12 subjects (young and elderly) at three contraction levels (10, 20 and 50% MVC), and matched simulation data. Performance was assessed via agreement between the two algorithms for experimental data and accuracy with respect to the known decomposition for simulated data. For the experimental data, median agreement between the Montreal and Fuzzy Expert algorithms at 10, 20 and 50% MVC was 95.7, 86.4 and 64.8%, respectively. For the simulation data, median accuracy was 99.8%, 100% and 95.9% for Montreal, and 100%, 98% and 93.5% for Fuzzy Expert at the different contraction levels.