Clinical Characterization of Electromyographic Data Using Computational Tools

This work presents a system intended to assist in the diagnosis of muscular disease based on the decomposition of electromyographic data through quantitative means. The performance of the system is evaluated both through quantitative analysis of correct suggestions as well as descriptively through an examination of the decision space presented in several pertinent examples. Data used for this analysis is synthetically created gold-standard data produced through a previously described muscle simulator and prepared through application of the DQEMG program. The design of the decision support system is based on a conceptual model reflecting the decision process present in clinical work; the provision of a drill-down exploration mechanism to allow investigation of the statistical support underlying the decision process allows access to the needed statistical confidences required for informed clinical diagnosis while providing summary data display to allow a clinician access to muscle-level characterizations in a reproducible and quantifiable way. The use of the "pattern discovery" algorithm provides a statistically sound mechanism for the generation of rules from a training data set. The exceptional performance observed here provides confidence in the system as a potential tool for use in clinical diagnosis