Neuromuscular Clinical Decision Support using Motor Unit Potentials Characterized by 'Pattern Discovery'
Pino, Lou Joseph
MetadataShow full item record
Objectives: Based on the analysis of electromyographic (EMG) data muscles are often characterized as normal or affected by a neuromuscular disease process. A clinical decision support system (CDSS) for the electrophysiological characterization of muscles by analyzing motor unit potentials (MUPs) was developed to assist physicians and researchers with the diagnosis, treatment & management of neuromuscular disorders and analyzed against criteria for use in a clinical setting. Methods: Quantitative MUP data extracted from various muscles from control subjects and patients from a number of clinics was used to compare the sensitivity, specificity, and accuracy of a number of different clinical decision support methods. The CDSS developed in this work known as AMC-PD has three components: MUP characterization using Pattern Discovery (PD), muscle characterization by taking the average of MUP characterizations and calibrated muscle characterizations. Results: The results demonstrated that AMC-PD achieved higher accuracy than conventional means and outlier analysis. Duration, thickness and number of turns were the most discriminative MUP features for characterizing the muscles studied in this work. Conclusions: AMC-PD achieved higher accuracy than conventional means and outlier analysis. Muscle characterization performed using AMC-PD can facilitate the determination of “possible”, “probable”, or “definite” levels of disease whereas the conventional means and outlier methods can only provide a dichotomous “normal” or “abnormal” decision. Therefore, AMC-PD can be directly used to support clinical decisions related to initial diagnosis as well as treatment and management over time. Decisions are based on facts and not impressions giving electromyography a more reliable role in the diagnosis, management, and treatment of neuromuscular disorders. AMC-PD based calibrated muscle characterization can help make electrophysiological examinations more accurate and objective.