An adaptive Bayesian algorithm for efficient auditory brainstem response threshold estimation: Numerical validation

Abstract

The auditory brainstem response (ABR) can be used to estimate the hearing threshold of animals or human subjects who are unable to respond to behavioral measures. However, ABR can be time-intensive and is vulnerable to human subjectivity while interpreting the waveforms. An adaptive procedure has been developed to efficiently and objectively estimate ABR threshold. The procedure iteratively fits a Gaussian process (GP) model to the ABR waveforms collected so far and optimizes the subsequent stimulus. The algorithm was validated through numerical simulation using pre-collectedhuman ABR at 0.5, 1.0, 2.0, and 4.0 kHz with levels from below threshold to 90 dB in 5 dB increments from a cohort of normal-hearing listeners. This led to a full stimulus space of approximately 55 stimuli per test ear. For each test ear, the ABR threshold was estimated by human raters based on the entire stimulus space. The ABR threshold was also estimated using the adaptive procedure, which iteratively sampled a subset (∼35%) of the stimulus space. At the end of the adaptive procedure, the fitted GP model was able to capture the individual differences in waveform morphology between listeners. Furthermore, the threshold estimates from repeated runs of the adaptive procedure demonstrated adequate test-retest reliability.