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Model-based hearing diagnosis of middle ear condition using inverse fuzzy arithmetic and artificial neuronal network

  • Due to the large interindividual variances and the poor optical accessibility of the ear, the specificity of hearing diagnostics today is severely restricted to a certain clinical picture and quantitative assessment. Often only a yes or no decision is possible, which depends strongly on the subjective assessment of the ENT physician. A novel approach, in which objectively obtainable, non invasive audiometric measurements are evaluated using a numerical middle ear model, makes it possible to make the hidden middle ear properties visible and quantifiable. The central topic of this paper is a novel parameter identification algorithm that combines inverse fuzzy arithmetic with an artificial neural network in order to achieve a coherent diagnostic overall picture in the comparison of model and measurement. Its usage is shown at a pathological pattern called malleus fixation where the upper ligament of the malleus is pathologically stiffened.

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Metadaten
Name:Sackmann, Benjamin; Priwitzer, Barbara; Lauxmann, Michael
URN:urn:nbn:de:bsz:rt2-opus4-24931
URL:https://www.curac.org/de/willkommen-2019/vergangene-jahrestagungen/84-vergangene-jahrestagungen/36-curac-2019
ISBN:978-3-00-063717-9
Erschienen in:CURAC 2019 - Tagungsband : 18. Jahrestagung der Deutschen Gesellschaft für Computer- und Roboterassistierte Chirurgie e. V. : 19. - 21. September 2019, Reutlingen
Publisher:Hochschule Reutlingen, Fakultät Informatik
Place of publication:Reutlingen
Editor:Oliver Burgert
Document Type:Conference Proceeding
Language:English
Year of Publication:2019
Tag:artificial neural network; fuzzy arithmetic; middle-ear model; model-based ENT diagnostic; wideband tympanometry
Pagenumber:6
First Page:89
Last Page:94
Catalogue entry:Im Katalog der Hochschule Reutlingen ansehen
Dewey Decimal Classification:610 Medizin, Gesundheit
004 Informatik
Open Access:Ja
Licence (German):License Logo  Open Access