Adaptive certainty-based classification for decomposition of EMG signals.
An adaptive certainty-based supervised classification approach for electromyographic (EMG) signal decomposition is presented and evaluated. Similarity criterion used for grouping motor unit potentials (MUPs) is based on a combination of MUP shapes and two modes of use of motor unit (MU) firing pattern information: passive and active. Performance of the developed classifier was evaluated using synthetic signals of known properties and real signals and compared with the performance of the certainty classifier (CC). Across the sets of simulated and real EMG signals used for comparison, the adaptive certainty classifier (ACC) had both better average performance and lower performance variability. For simulated signals of varying intensity, the ACC had an average correct classification rate (CCr ) of 83.7% with a mean absolute deviation (MAD) of 5.8% compared to 78.3 and 8.7%, respectively, for the CC. For simulated signals with varying amounts of shape and/or firing pattern variability, the ACC had a CCr of 79.7% with a MAD of 4.7% compared to 76.6 and 6.9%, respectively, for the CC. For real signals, the ACC had a CCr of 70.0% with a MAD of 6.3% compared to 64.9 and 6.4%, respectively, for the CC. The test results demonstrate that the ACC can manage both MUP shape variability as well as MU firing pattern variability. The ACC adapts to EMG signal characteristics to create dynamic data driven classification criteria so that the number of MUP assignments made reflects the signal complexity and the number of erroneous assignments is kept sufficiently low. The ability of the ACC to adjust to specific signal characteristics suggests that it can be successfully applied to a wide variety of EMG signals.
Rasheed
Sarbast
S
Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada. srasheed@engmail.uwaterloo.ca
Stashuk
Daniel
D
Kamel
Mohamed
M
D003198:Computer Simulation; D004576:Electromyography; D017143:Fuzzy Logic; D006801:Humans; D008954:Models, Biological; D009046:Motor Neurons / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology; D012815:Signal Processing, Computer-Assisted