Neural networks and neurophysiological signals

Abstract

The purpose of this thesis project is to develop, implement, and validate a neural network which will classify compound muscle action potentials (CMAPs). The two classes of signals are "via­ble" and "non-viable." This classification system will be used as part of a quality assurance mech­anism on the NC-stat nerve conduction monitoring system. The results show that standard backpropagation neural networks provide exceptional classification results on novel waveforms. Also, principal components analysis is a powerful preprocessing technique which allows for a sig­nificant reduction in processing efficiency, while maintaining performance standards. This system is implementable as a real-time quality control process for the NC-stat.