A knowledge based interpretation system for EMG abnormalities.
The conventional method of diagnosis in electromyography is complex and time consuming, not only due to the large number of parameters, to be considered for diagnosis, but also because of the usual procedure of evaluating the different parameters of EMG signal by visual scanning of the plotted signal. So there is a clear need to make use of computer aided decision support system. In the present work an attempt has been made in the direction of integration into one automated system, the qualitative knowledge of the physician, with possibly sophisticated signal analysis tools which must replace the visual scanning. A software program (in Turbo-C) on a PC-AT has been developed to evaluate the different parameters of MUAP's (motor unit action potential) in a EMG signal. Then an Expert system (in Turbo-Prolog) has been implemented for diagnostic purposes of different muscular abnormalities by making a knowledge base from the different parameters involved in the decision making procedure of clinical electromyography. A hybrid model of rule and frame based Expert system is implemented. An attempt has been made for making a complete system, i.e., for recording, analysis and decision making for diagnosis.
Mishra
R B
RB
Department of Electrical Engineering, Banaras Hindu University, Varanasi, India.
Dandapat
S
S
D003936:Diagnosis, Computer-Assisted; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D009135:Muscular Diseases / Q000175:diagnosis; D009136:Muscular Dystrophies / Q000175:diagnosis; D009157:Myasthenia Gravis / Q000175:diagnosis; D009422:Nervous System Diseases / Q000175:diagnosis; D010363:Pattern Recognition, Automated; D011051:Poliomyelitis / Q000175:diagnosis; D017285:Polymyositis / Q000175:diagnosis; D012984:Software; D012985:Software Design