title: Rigorous a posteriori assessment of accuracy in EMG decomposition.
abstract: If electromyography (EMG) decomposition is to be a useful tool for scientific investigation, it is essential to know that the results are accurate. Because of background noise, waveform variability, motor-unit action potential (MUAP) indistinguishability, and perplexing superpositions, accuracy assessment is not straightforward. This paper presents a rigorous statistical method for assessing decomposition accuracy based only on evidence from the signal itself. The method uses statistical decision theory in a Bayesian framework to integrate all the shape- and firing-time-related information in the signal to compute an objective a posteriori measure of confidence in the accuracy of each discharge in the decomposition. The assessment is based on the estimated statistical properties of the MUAPs and noise and takes into account the relative likelihood of every other possible decomposition. The method was tested on 3 pairs of real EMG signals containing 4-7 active MUAP trains per signal that had been decomposed by a human expert. It rated 97% of the identified MUAP discharges as accurate to within ± 0.5 ms with a confidence level of 99%, and detected six decomposition errors. Cross-checking between signal pairs verified all but two of these assertions. These results demonstrate that the approach is reliable and practical for real EMG signals.
authors.affiliation: Rehabilitation Research and Development Center, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA. mcgill@va51.stanford.edu
authors.forename: Kevin C
authors.initials: KC
authors.lastname: McGill
authors.forename: Hamid R
authors.initials: HR
authors.lastname: Marateb
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004576:Electromyography / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D006801:Humans; D009046:Motor Neurons / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*