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  âˆ¼ 50% for the GP compared to a sequentially applied Hotelling's T2 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.

Controlling test specificity for auditory evoked response detection using a frequency domain bootstrap.

BACKGROUND: Statistical detection methods are routinely used to automate auditory evoked response (AER) detection and assist clinicians with AER measurements. However, many of these methods are built around statistical assumptions that can be violated for AER data, potentially resulting in reduced or unpredictable test performances. This study explores a frequency domain bootstrap (FDB) and some FDB modifications to preserve test performance in serially correlated non-stationary data. METHOD: The FDB aims to generate many surrogate recordings, all with similar serial correlation as the original recording being analysed. Analysing the surrogates with the detection method then gives a distribution of values that can be used for inference. A potential limitation of the conventional FDB is the assumption of stationary data with a smooth power spectral density (PSD) function, which is addressed through two modifications. COMPARISONS WITH EXISTING METHODS: The FDB was compared to a conventional parametric approach and two modified FDB approaches that aim to account for heteroskedasticity and non-smooth PSD functions. Hotelling's T2(HT2) test applied to auditory brainstem responses was the test case. RESULTS: When using conventional HT2, false-positive rates deviated significantly from the nominal alpha-levels due to serial correlation. The false-positive rates of the modified FDB were consistently closer to the nominal alpha-levels, especially when data was strongly heteroskedastic or the underlying PSD function was not smooth due to e.g. power lines noise. CONCLUSION: The FDB and its modifications provide accurate, recording-dependent approximations of null distributions, and an improved control of false-positive rates relative to parametric inference for auditory brainstem response detection.

A group sequential test for ABR detection.

Objective: To detect the auditory brainstem response (ABR) automatically using an innovative sequentially applied Hotelling's T 2 test, with the overall goal of optimising test time whilst controlling the false-positive rate (FPR). Design: The stage-wise critical decision boundaries for accepting or rejecting the null hypothesis were found using a new approach called the Convolutional Group Sequential Test (CGST). Specificity, sensitivity, and test time were evaluated using simulations and subject recorded data. Study sample: Data consists of click-evoked ABR threshold series from 12 normal hearing adults, and recordings of EEG background activity from 17 normal hearing adults. Results: Reductions in mean test time of up to 40-45% were observed for the sequential test, relative to a conventional "single shot" test where the statistical test is applied to the data just once. To obtain these results, it will occasionally be necessary to run the test to a higher number of stimuli, i.e. the maximum test time needs to be increased. Conclusions: The CGST can be used to control the specificity of a sequentially applied ABR detection method. Doing so can reduce test time, relative to the "single shot" test, when considered across a cohort of test subjects.

Objective measures for detecting the auditory brainstem response: comparisons of specificity, sensitivity and detection time.

OBJECTIVE: To evaluate and compare the specificity, sensitivity and detection time of various time-domain and multi-band frequency domain methods when detecting the auditory brainstem response (ABR). DESIGN: Simulations and subject recorded data were used to assess and compare the performance of the Hotelling's T2 test (applied in either time or frequency domain), two versions of the modified q-sample uniform scores test and both the Fsp and Fmp, which were evaluated using both conventional F-distributions with assumed degrees of freedom and a bootstrap approach. STUDY SAMPLE: Data consisted of click-evoked ABRs and recordings of EEG background activity from 12 to 17 normal hearing adults, respectively. RESULTS: An overall advantage in sensitivity and detection time was demonstrated for the Hotelling's T2 test. The false-positive rates (FPRs) of the Fsp and Fmp were also closer to the nominal alpha-level when evaluating statistical significance using the bootstrap approach, as opposed to using conventional F-distributions. The FPRs of the remaining methods were slightly higher than expected. CONCLUSIONS: In this work, Hotelling's T2 outperformed the alternative methods for automatically detecting ABRs. Its promise as a sensitive and efficient detection method should now be tested in a larger clinical study.