• Login
    View Item 
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Master's Theses
    • View Item
    •   Home
    • UA Graduate and Undergraduate Research
    • UA Theses and Dissertations
    • Master's Theses
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

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

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    azu_td_1346419_sip1_m.pdf
    Size:
    4.051Mb
    Format:
    PDF
    Download
    Author
    Narus, Scott Patrick, 1963-
    Issue Date
    1991
    Keywords
    Engineering, Biomedical.
    Engineering, Electronics and Electrical.
    Artificial Intelligence.
    Advisor
    Mylrea, Kenneth C.
    
    Metadata
    Show full item record
    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)
    Degree Name
    M.S.
    Degree Level
    masters
    Degree Program
    Graduate College
    Degree Grantor
    University of Arizona
    Collections
    Master's Theses

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.