Bayesian aggregation versus majority vote in the characterization of non-specific arm pain based on quantitative needle electromyography.
BACKGROUND
Methods for the calculation and application of quantitative electromyographic (EMG) statistics for the characterization of EMG data detected from forearm muscles of individuals with and without pain associated with repetitive strain injury are presented.


METHODS
A classification procedure using a multi-stage application of Bayesian inference is presented that characterizes a set of motor unit potentials acquired using needle electromyography. The utility of this technique in characterizing EMG data obtained from both normal individuals and those presenting with symptoms of "non-specific arm pain" is explored and validated. The efficacy of the Bayesian technique is compared with simple voting methods.


RESULTS
The aggregate Bayesian classifier presented is found to perform with accuracy equivalent to that of majority voting on the test data, with an overall accuracy greater than 0.85. Theoretical foundations of the technique are discussed, and are related to the observations found.


CONCLUSIONS
Aggregation of motor unit potential conditional probability distributions estimated using quantitative electromyographic analysis, may be successfully used to perform electrodiagnostic characterization of "non-specific arm pain." It is expected that these techniques will also be able to be applied to other types of electrodiagnostic data.
School of Rehabilitation Therapy, Queen's University, Kingston, Ontario, Canada. andrewhw@ieee.org
Andrew
A
Hamilton-Wright
Linda
L
McLean
Daniel W
DW
Stashuk
Kristina M
KM
Calder
D000328:Adult; D000465:Algorithms; D001132:Arm; D001499:Bayes Theorem; D012090:Cumulative Trauma Disorders / Q000503:physiopathology; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008875:Middle Aged; D018482:Muscle, Skeletal / Q000503:physiopathology*; D009339:Needles; D010146:Pain / Q000503:physiopathology*; D012815:Signal Processing, Computer-Assisted