Characterization of neural network simulations for optimal classification of intraoperative electroencephalograph data

Persistent Link:
http://hdl.handle.net/10150/278002
Title:
Characterization of neural network simulations for optimal classification of intraoperative electroencephalograph data
Author:
Narus, Scott Patrick, 1963-
Issue Date:
1991
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
Accurately and consistently determining depth of anesthesia during surgical procedures is a significant problem. A more objective technique than traditional methods is required. Different concentrations of anesthetic drugs have been shown to affect the Electroencephalograph; results, however, are inconsistent when using only visual inspection of the EEG. An automated technique using Neural Networks for classifying anesthetic depth from EEG data is proposed. Neural Networks are reviewed. Reasons for choosing a Backpropagation Network (BPN) are discussed. Ambiguities in previous BPN research are presented. Over 3,000 networks are formed, demonstrating training and classification properties while altering network topologies, parameters and performance criteria. Tests are performed on the Power spectrum and Phase portions of the EEG data. Optimal BPN parameters and topologies are shown. Results are compared with a statistical paradigm.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Biomedical.; Engineering, Electronics and Electrical.; Artificial Intelligence.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Mylrea, Kenneth C.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleCharacterization of neural network simulations for optimal classification of intraoperative electroencephalograph dataen_US
dc.creatorNarus, Scott Patrick, 1963-en_US
dc.contributor.authorNarus, Scott Patrick, 1963-en_US
dc.date.issued1991en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAccurately and consistently determining depth of anesthesia during surgical procedures is a significant problem. A more objective technique than traditional methods is required. Different concentrations of anesthetic drugs have been shown to affect the Electroencephalograph; results, however, are inconsistent when using only visual inspection of the EEG. An automated technique using Neural Networks for classifying anesthetic depth from EEG data is proposed. Neural Networks are reviewed. Reasons for choosing a Backpropagation Network (BPN) are discussed. Ambiguities in previous BPN research are presented. Over 3,000 networks are formed, demonstrating training and classification properties while altering network topologies, parameters and performance criteria. Tests are performed on the Power spectrum and Phase portions of the EEG data. Optimal BPN parameters and topologies are shown. Results are compared with a statistical paradigm.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Biomedical.en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectArtificial Intelligence.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorMylrea, Kenneth C.en_US
dc.identifier.proquest1346419en_US
dc.identifier.bibrecord.b27226566en_US
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