Neural networks and neurophysiological signals
Name
42996955-MIT.pdf
Description
Full printable version
Size
2.62 MB
Format
Adobe PDF
Checksum (MD5)
4cb5b0c16d388551491cf3be0095e90b
Author(s)
Sarda, Srikant, 1977-
Advisor(s)
Steve Burns.
Date Issued
1999
Publisher
Massachusetts Institute of Technology
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.
Description
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes bibliographical references (p. 45).
Subjects
Electrical Engineering and Computer Science
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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