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Remote audiogram and notched-noise tests using Gaussian processes.

Emanuele Perugia1, Michael A Stone1,2, Karolina Kluk1

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Summary
This summary is machine-generated.

Remote hearing tests using Bayesian methods show potential for tracking hearing loss and auditory filter width. However, equipment calibration issues limited accuracy in this study.

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Area of Science:

  • Audiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Accurate hearing assessment is crucial for managing age-related hearing loss.
  • Remote testing offers convenience but faces challenges in reliability and calibration.
  • Bayesian active-learning methods show promise for optimizing audiological tests.

Purpose of the Study:

  • To develop and validate remote hearing detection (audiogram) and discrimination (notched-noise) tests.
  • To utilize Bayesian active-learning Gaussian processes for binary discrimination of sounds.
  • To select optimal stimulus parameters for remote audiological assessments.

Main Methods:

  • Developed remote audiogram and notched-noise tests employing Bayesian active-learning Gaussian processes.
  • Recruited 42 participants (48-75 years) with known pure-tone audiometry results.
  • Assessed agreement using Bland-Altman plots and concordance correlation coefficients (CCC).

Main Results:

  • Agreement between true and estimated hearing thresholds was poor to fair (CCC=0.19-0.34), attributed to uncalibrated participant equipment.
  • Estimated equivalent rectangular bandwidths (ERBs) correlated significantly with pure-tone audiometry at 1000 Hz (rs=0.36).
  • Remote tests collectively tracked hearing threshold breakpoints, slopes, and auditory filter width trends.

Conclusions:

  • Remote hearing detection and discrimination tests show potential for monitoring hearing characteristics.
  • Equipment calibration is critical for improving the accuracy of remote audiological assessments.
  • The developed methods can provide insights into auditory filter shape and hearing thresholds remotely.