Neural networks and neurophysiological signals

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

1999

Author dc:creator

• Sarda, Srikant, 1977-

Advisor dc:contributor.advisor

• Steve Burns.

• Electrical Engineering and Computer Science

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/9806

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/9806