{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/9806"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/9806","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"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. 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