2026-09-24T01:39:14Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/98062026-06-05T20:27:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131024
Steve Burns.
Sarda, Srikant, 1977-
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-08-19T20:18:29Z
2005-08-19T20:18:29Z
1999
1999
http://hdl.handle.net/1721.1/9806
42996955
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 45).
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.
by Srikant Sarda.
S.B.
M.Eng.
46 p.
2750840 bytes
2750597 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
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.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science
Neural networks and neurophysiological signals
Thesis
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