Intelligent classification in EMG decomposition

This paper presents research relating to the use of computers for the intelligent decomposition of myoelectric signals (EMG). A knowledge based expert system is described which decomposes superimposed waveforms formed from overlapping motor unit action potentials (MUAPs) in a myoelectric signal using symbolic information provided by numerical recognition analysis. The system, written in Prolog, consists of some 30 rules in the knowledge base that are driven by an interpreter that incorporates uncertain reasoning based on fuzzy set theory. The expert system contains both procedural and declarative knowledge representations of the problem domain. The declarative rules contain a description of the relationships between the raw motor unit (MU) information collected by the numerical analysis and the superimposed waveforms being decomposed. The procedural rules interact with the declarative rules through rule attachments that activate demon procedures. The demon procedure computes fuzzy certainty factors for all the possible combinations of MUAPs that form a superimposed waveform.