Neural networks and neurophysiological signals
Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/9806 |
| _version_ | 1811078793146662912 |
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| author | Sarda, Srikant, 1977- |
| author2 | Steve Burns. |
| author_facet | Steve Burns. Sarda, Srikant, 1977- |
| author_sort | Sarda, Srikant, 1977- |
| collection | MIT |
| description | Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999. |
| first_indexed | 2024-09-23T11:05:34Z |
| format | Thesis |
| id | mit-1721.1/9806 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:05:34Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/98062020-04-07T21:42:58Z Neural networks and neurophysiological signals Sarda, Srikant, 1977- Steve Burns. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science 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.and M.Eng. 2005-08-19T20:18:29Z 2005-08-19T20:18:29Z 1999 1999 Thesis http://hdl.handle.net/1721.1/9806 42996955 eng 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 46 p. 2750840 bytes 2750597 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science Sarda, Srikant, 1977- Neural networks and neurophysiological signals |
| title | Neural networks and neurophysiological signals |
| title_full | Neural networks and neurophysiological signals |
| title_fullStr | Neural networks and neurophysiological signals |
| title_full_unstemmed | Neural networks and neurophysiological signals |
| title_short | Neural networks and neurophysiological signals |
| title_sort | neural networks and neurophysiological signals |
| topic | Electrical Engineering and Computer Science |
| url | http://hdl.handle.net/1721.1/9806 |
| work_keys_str_mv | AT sardasrikant1977 neuralnetworksandneurophysiologicalsignals |