Optimising weighted averaging for auditory brainstem response detection

Abstract

The auditory brainstem response (ABR) is a clinical test used to evaluate hearing objectively. The aim of this study was to optimise weighted averaging for both residual noise reduction and also for objective ABR detection using the Fmp statistical test. Analyses were performed using no-stimulus EEG background activity recorded from 15 participants and simulated “response present” data (4,602 ensembles in total). Different approaches for estimating the variance of the noise within each block were compared, as was the effect of the number of recording epochs in each block when calculating and applying the weights. The “VAR Whole Block” method was found to be more effective than the “VAR MP” method at estimating the noise level, especially for smaller block sizes (2–10 epochs). Caution should be exerted when selecting recording parameters for use with weighted averaging as an inflation in the “response absent” Fmp statistic was observed using small block sizes (relative to unweighted averaging); this may be due to a bias in the Fmp statistic observed as a result of the combined effects of the finite Fmp analysis window length and the high-pass filter setting. Optimised weighted averaging was effective in reducing the mean residual noise level in the averaged waveform, leading to improved ABR detection. Further work is required to optimise the Fmp analysis window length, recording settings, and weighted averaging parameters in combination, using a large clinical dataset.