Diagnosis of Muscle Condition on the Basis of MUP Spectral Analysis

Electromyography (EMG) is a functional examination that plays a fundamental role in the diagnosis of neuromuscular disorders. The method allows for distinction between records of a healthy muscle and a changed muscle as well as for determination of whether pathological changes are of primary myopathic or neuropathic character. The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologist's experience. Then, selected temporal parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from, among other things, the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for single-point spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. Therefore, the definition fulfills a fundamental criterion for the parameter used for preparation of a standard. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.