Application of Classification Models to Pharyngeal High-Resolution Manometry.
Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ),...
| Publicado en: | Journal of Speech, Language & Hearing Research Vol. 55; no. 3; pp. 892 - 903 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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American Speech-Language-Hearing Association
Jun2012
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=76502736&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 76502736 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10924388 1SM jtl: Journal of Speech, Language & Hearing Research issn: 10924388 maglogo: N pubinfo: dt: Jun2012 vid: 55 iid: 3 pid: 42 pub: American Speech-Language-Hearing Association artinfo: ui: 76502736 10.1044/1092-4388(2011/11-0088) ppf: 892 ppct: 11 formats: fmt: @attributes: type: P size: 378KB tig: atl: Application of Classification Models to Pharyngeal High-Resolution Manometry. aug: au: Mielens, Jason D. Hoffman, Matthew R. Ciucci, Michelle R. McCulloch, Timothy M. Jianga, Jack J. affil: University of Wisconsin School of Medicine and Public Health, Madison University of Wisconsin-Madison su: Case-control method Deglutition disorders Pharynx physiology Experimental design Factor analysis Magnetic resonance imaging Manometers Research funding Diagnosis sug: subj: Case-control method Diagnostic Imaging Centers Deglutition disorders Pharynx physiology Experimental design Factor analysis Magnetic resonance imaging Manometers Research funding Diagnosis keyword: artificial neural network classification model deglutition dysphagia high-resolution manometry pharyngeal manometry artificial neural network classification model deglutition dysphagia high-resolution manometry pharyngeal manometry ab: Purpose: The authors present 3 methods of performing pattern recognition on spatiotemporal plots produced by pharyngeal high-resolution manometry (HRM). Method: Classification models, including the artificial neural networks (ANNs) multilayer perceptron (MLP) and learning vector quantization (LVQ), as well as support vector machines (SVM), were evaluated for their ability to identify disordered swallowing. Data were collected from 12 control subjects and 13 subjects with swallowing disorders; for this experiment, these subjects swallowed 5-ml water boluses. Following extraction of relevant parameters, a subset of the data was used to train the models, and the remaining swallows were then independently classified by the networks. Results: All methods produced high average classification accuracies, with MLP, SVM, and LVQ achieving accuracies of 96.44%, 91.03%, and 85.39%, respectively. When evaluating the individual contributions of each parameter and groups of parameters to the classification accuracy, parameters pertaining to the upper esophageal sphincter were most valuable. Conclusion: Classification models show high accuracy in segregating HRM data sets and represent 1 method of facilitating application of HRM to the clinical setting by eliminating the time required for some aspects of data extraction and interpretation. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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