Abstract
The aim of this study is to analyse an effective wavelet method for denoising and tracking temporal variations of the auditory brainstem response (ABR). The rapid and accurate extraction of ABRs in clinical practice has numerous benefits, including reductions in clinical test times and potential long-term patient monitoring applications. One method of achieving rapid extraction is through the application of wavelet filtering which, according to earlier research, has shown potential in denoising signals with low signal-to-noise ratios. The research documented in this paper evaluates the application of three such wavelet approaches on a common set of ABR data collected from eight participants. We introduced the use of the latency–intensity curve of ABR wave V for performance evaluation of tracking temporal variations. The application of these methods to the ABR required establishing threshold functions and time windows as an integral part of the research. Results revealed that the cyclic-shift-tree-denoising performed superior compared to other tested approaches. This required an ensemble of only 32 epochs to extract a fully featured ABR compared to the 1024 epochs with conventional ABR extraction based on linear moving time averaging.
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