These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Model-based hearing diagnostics based on wideband tympanometry measurements utilizing fuzzy arithmetic.
    Author: Sackmann B, Dalhoff E, Lauxmann M.
    Journal: Hear Res; 2019 Jul; 378():126-138. PubMed ID: 30878271.
    Abstract:
    Today's audiometric methods for the diagnosis of middle ear disease are often based on a comparison of measurements with standard curves, that represent the statistical range of normal hearing responses. Because of large inter-individual variances in the middle ear, especially in wideband tympanometry (WBT), specificity and quantitative evaluation are greatly restricted. A new model-based approach could transform today's predominantly qualitative hearing diagnostics into a quantitative and tailored, patient-specific diagnosis, by evaluating WBT measurements with the aid of a middle-ear model. For this particular investigation, a finite element model of a human ear was used. It consisted of an acoustic ear canal and a tympanic cavity model, a middle-ear with detailed nonlinear models of the tympanic membrane and annular ligament, and a simplified inner-ear model. This model has made it possible for us to simulate pathologies like the stiffening of ligaments or joints, because we can simply change the corresponding mechanical parameters of the model. On the other hand, it is also possible to identify pathologies from measurements, by analyzing the parameters obtained by a system identification procedure. This reduces the number of required model parameters through sensitivity studies and parameter clustering. Uncertainties due to the lack of knowledge, subjectivity in numerical implementation and model simplification are taken into account by the application of fuzzy arithmetic. The most confident parameter set can be determined by applying an inverse fuzzy method on the measurement data. The principle and the benefits of this model-based approach are illustrated by the example of a two-mass oscillator, and also by the simulation of the energy absorbance of an ear with malleus fixation, where the parameter changes that are introduced can be determined quantitatively through the system identification.
    [Abstract] [Full Text] [Related] [New Search]