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Estimating hearing aid fitting presets with machine learning-based clustering strategies.

Chelzy Belitz1, Hussnain Ali1, John H L Hansen1

  • 1Center for Robust Speech Systems, The University of Texas at Dallas, Richardson, Texas, 75075 USA.

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Summary

Many hearing impaired individuals do not use hearing aids (HA) due to various factors. This study uses machine learning on audiogram data to find better starting HA configurations, aiming to improve user retention.

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Area of Science:

  • Audiology
  • Machine Learning
  • Biomedical Data Science

Background:

  • Approximately 35 million Americans experience hearing impairment.
  • Only 25% of hearing impaired individuals utilize hearing aids (HA).
  • Factors like performance variability, lengthy setup, cost, and unmet expectations hinder HA adoption.

Purpose of the Study:

  • To analyze a national dataset of pure-tone audiograms and HA fitting configurations.
  • To leverage machine learning for identifying optimal initial HA settings.
  • To reduce the time required to achieve effective HA configurations and enhance user retention.

Main Methods:

  • Nationwide dataset analysis of pure-tone audiograms.
  • Application of machine learning clustering techniques.
  • Examination of HA fitting configurations.

Main Results:

  • Characterization of a nationwide dataset for hearing aid fitting.
  • Identification of potential starting configurations through data clustering.
  • Demonstration of a method to reduce time-to-convergence for HA optimization.

Conclusions:

  • Machine learning clustering can suggest effective initial hearing aid (HA) configurations.
  • Optimizing starting configurations can significantly decrease the time to achieve desired performance.
  • Reducing setup time is a key strategy to improve hearing aid retention rates among users.