Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/5958
Authors: Wadhwani, Arun Kumar
Issue Date: 1993
Abstract: This dissertation deals with an expert system which has been developed for automatic identification of different muscle disorders, after extracting and characterising motor unit action potential (MUAP'S) from the electromyo graphic pattern for clinical as well as research application. It also presents how these features of MUAP's will be useful in building an expert system for the interpretation purposes. The first chapter deals with the importance and utility of EMG signal. The second chapter gives a brief description of the muscle physiology including the characteristics of MUAP, Electrodes and - recording and applications of EKG signal. The Third chapter presents the structure of an expert system, knowledge representation schemes, Advantages, Ranges and Limitations of KBES. The chapter four describes the clinically important EMG parameters and various methods of its measurement. the fifth chapter has three sections. The first concerns with over view of knowledge based system created from literature, second part involves procedure to acquire current state and third section covers the Inference Engine i.e. the rules used and interpretation procedure, Results, discussion and provisions of modification. The last sixth chapter is the ,about the conclusions and scope for future work. The inference drawn from the previous chapters are enumerated. The limitation of the work and the scope of expanding the work further are given. Basically this dissertation is an effort to explore new grounds for a better understanding of a technique to study the electrophysiology of muscles leading not only to precocious diagnosis and research but to study also the effects of muscular fibers on different controlled conditions of neuromuscular system.
Other Identifiers: M.Tech
Research Supervisor/ Guide: Saxena, S. C.
metadata.dc.type: M.Tech Dessertation
Appears in Collections:MASTERS' THESES (Electrical Engg)

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