Classification of glottic insufficiency and tension asymmetry using a multilayer perceptron.
Objective: Laryngeal function can be evaluated from multiple perspectives, including aerodynamic input, acoustic output, and mucosal wave vibratory characteristics. To determine the classifying power of each of these, we used a multilayer perceptron artificial neural network (ANN) to classify data a...
| Publicado en: | Laryngoscope Vol. 122; no. 12; pp. 2773 - 2781 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Dec2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104393607&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104393607 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0023852X 1GR jtl: Laryngoscope issn: 0023852X maglogo: Y pubinfo: dt: Dec2012 vid: 122 iid: 12 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104393607 NLM23070824 2011791418 10.1002/lary.23549 NLM23070824 PMC3522789 104393607 ppf: 2773 ppct: 8 formats: tig: atl: Classification of glottic insufficiency and tension asymmetry using a multilayer perceptron. aug: au: Hoffman MR Surender K Devine EE Jiang JJ Hoffman, Matthew R Surender, Ketan Devine, Erin E Jiang, Jack J affil: Department of Surgery, Division of Otolaryngology - Head and Neck Surgery, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA sug: subj: Glottis Physiopathology Multilayer Perceptrons Vocal Cords Physiopathology Voice Disorders Classification Voice Quality Physiology Acoustics Animal Studies Dogs Models, Biological Reproducibility of Results ROC Curve Voice Disorders Physiopathology Funding Source ab: Objective: Laryngeal function can be evaluated from multiple perspectives, including aerodynamic input, acoustic output, and mucosal wave vibratory characteristics. To determine the classifying power of each of these, we used a multilayer perceptron artificial neural network (ANN) to classify data as normal, glottic insufficiency, or tension asymmetry.Study Design: Case series analyzing data obtained from excised larynges simulating different conditions.Methods: Aerodynamic, acoustic, and videokymographic data were collected from excised canine larynges simulating normal, glottic insufficiency, and tension asymmetry. Classification of samples was performed using a multilayer perceptron ANN.Results: A classification accuracy of 84% was achieved when including all parameters. Classification accuracy dropped below 75% when using only aerodynamic or acoustic parameters and below 65% when using only videokymographic parameters.Conclusions: Samples were classified with the greatest accuracy when using a wide range of parameters. Decreased classification accuracies for individual groups of parameters demonstrate the importance of a comprehensive voice assessment when evaluating dysphonia. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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