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Comparative Modal Analysis of the Tympanic Membrane Mechanics Between Normal and Experimentally Simulated Pathological Ears

  • Haimi TangEmail author
  • Payam Razavi
  • Nima Maftoon
  • John J. Rosowski
  • Cosme Furlong
  • Jeffrey T. Cheng
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

We are developing a High-speed Digital Holographic (HDH) system to measure acoustically induced transient displacements and shapes of live mammalian Tympanic Membrane (TM) for research and clinical applications. Currently, the HDH system measures the shape of the entire TM with a resolution of about 120 μm, and sound-induced displacements with magnitude resolutions of <10 nm and temporal resolutions of <20 μs in full-field of view. In this paper, we apply Experimental Modal Analysis (EMA) to the HDH results to extract modal parameters of the TM and quantitatively compare these parameters among different TMs of normal and pathological middle ears. Transient displacements in response to broadband acoustic clicks and the shape of cadaveric human TMs are measured before and after different simulated middle-ear pathologies including various levels of fluid in the middle ear cavity, stapes fixation, and incudo-stapedial (IS) joint interruption. The transient displacement of the TM along the sensitivity vector is supplemented with the 3-D TM shape information to derive the true displacement of the TM locally normal to the TM surface. The displacement is then used in an EMA framework to determine natural frequencies, damping and mode shapes of the TMs under the normal and different pathological middle-ear conditions.

Preliminary results show that the damping ratio of the TM at each natural frequency in the normal ear decreases as frequency increases. We also see differences in identified modal shapes and natural frequencies before and after various manipulations. We plan to identify trends in the data associated with different pathologies as well as test the sensitivity and selectivity of these analyses for clinical diagnosis. Due to the large amount of the data obtained from the HDH, Artificial Intelligence (AI) and Data Mining methods will be used to automate the EMA process and assist in the separation of normal and diseased states.

Keywords

Artificial intelligence (AI) Experimental modal analysis High-speed digital holography Human tympanic membrane Middle-ear pathologies 

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Copyright information

© Society for Experimental Mechanics, Inc. 2020

Authors and Affiliations

  • Haimi Tang
    • 1
    • 2
    Email author
  • Payam Razavi
    • 1
    • 2
  • Nima Maftoon
    • 3
  • John J. Rosowski
    • 4
    • 5
  • Cosme Furlong
    • 1
    • 2
    • 4
    • 5
  • Jeffrey T. Cheng
    • 4
    • 5
  1. 1.Center for Holographic Studies and Laser micro-mechaTronics (CHSLT)WorcesterUSA
  2. 2.Mechanical Engineering DepartmentWorcester Polytechnic InstituteWorcesterUSA
  3. 3.Systems Design Engineering DepartmentUniversity of WaterlooWaterlooCanada
  4. 4.Eaton-Peabody LaboratoryMassachusetts Eye and Ear InfirmaryBostonUSA
  5. 5.Department of Otology and LaryngologyHarvard Medical SchoolBostonUSA

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