Gaussian Processes for Hearing Threshold Estimation Using Auditory Brainstem Responses

The Auditory Brainstem Response (ABR) plays an important role in diagnosing and managing hearing loss, but can be challenging and time-consuming to measure. Test times are especially long when multiple ABR measurements are needed, e.g., when estimating hearing threshold at a range of frequencies. While many detection methods have been developed to reduce ABR test times, the majority were designed to detect the ABR at a single stimulus level and do not consider correlations in ABR waveforms across levels. These correlations hold valuable information, and can be exploited for more efficient hearing threshold estimation. This was achieved in the current work using a Gaussian Process (GP), i.e., a Bayesian approach for non-linear regression. The function to estimate with the GP was the ABR's amplitude across stimulus levels, from which hearing threshold was ultimately inferred. Active learning rules were also designed to automatically adjust the stimulus level and efficiently locate hearing threshold. Simulation results show test time reductions of up to
$\sim$
50% for the GP compared to a sequentially applied Hotelling's T
²
test, which does not consider correlations across ABR waveforms. A case study was also included to briefly assess the GP approach in ABR data from an adult volunteer.