Neuromuscular discrimination by aggregating information in motor unit potentials

When classifying data obtained by multiply sampling the same underlying entity, it is expected that characterizations based on aggregation of the information provided by all of the samples will be more accurate. Electromyographic (EMG) data presents such a scenario when individual motor unit potential trains (MUPTs) detected in a muscle during voluntary contraction are examined. We present a MUPT characterization technique based on quantitative analysis of EMG data, and further examine the value of various systems of aggregating MUPT information in order to achieve an overall electrophysiological assessment of the muscle. The data underlying this study was drawn from a population of patients who exhibit forearm pain associated with repetitive strain injury, and it is expected that these results will assist in electrophysiologically characterizing muscles of individuals having this condition and distinguishing these muscles from those of normative individuals and, in future, those suffering from other related conditions.