The diagnostic power of motor unit potential analysis: an objective bayesian approach.

Abstract

The notion of a "myopathic" or "neuropathic" electromyogram (EMG) is usually based on qualitative visual and acoustical impressions. Conventional quantification defines abnormality but not diagnosis, which requires interpretation of patterns of change. Discriminant analysis is a model for this multivariate decision. It tells how probable it is that a motor unit potential (MUP) comes from a normal, myopathic, or neuropathic muscle. Accumulation of single MUP information by a sequential Bayesian algorithm produced diagnostic probabilities above 0.95 in 91% of all muscles (223 biceps brachii muscles from 80 patients with motoneuron disorders, 56 patients with neuropathies, 71 patients with myopathies, and 34 controls). Two muscles from patients with neurogenic disorders were misclassified as "myopathic." Misclassification was more frequent only in myositis (4 of 28 muscles) and in oculopharyngeal muscular dystrophy (2 of 4 muscles). MUP discriminant classification was as sensitive as, and more specific than, conventional quantitative EMG, which discriminated between myopathic and neuropathic in only 22% of the muscles. This rate was 59% for discriminant analysis. As a knowledge-based expert system, MUP discriminant analysis successfully distinguishes between myopathic, neuropathic, and unclassifiable MUP samples. It discloses more information than conventional quantitative MUP analysis.