Automated extraction of auditory brainstem response latencies and amplitudes by means of non-linear curve registration.

Autor: Krumbholz K; School of Medicine, Hearing Sciences Group, University of Nottingham, United Kingdom. Electronic address: Katrin.Krumbholz@nottingham.ac.uk., Hardy AJ; School of Medicine, Hearing Sciences Group, University of Nottingham, United Kingdom; School of Psychology, University of Nottingham, United Kingdom., de Boer J; School of Medicine, Hearing Sciences Group, University of Nottingham, United Kingdom.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2020 Nov; Vol. 196, pp. 105595. Date of Electronic Publication: 2020 Jun 10.
DOI: 10.1016/j.cmpb.2020.105595
Abstrakt: Background and Objectives: Animal results have suggested that auditory brainstem responses (ABRs) to transient sounds presented at supra-threshold levels may be useful for measuring hearing damage that is hidden to current audiometric tests. Evaluating such ABRs requires extracting the latencies and amplitudes of relevant deflections, or "waves". Currently, this is mostly done by human observers manually picking the waves' peaks and troughs in each individual response - a process that is both time-consuming and requiring of expert experience. Here, we propose a highly automated procedure for extracting individual ABR wave latencies and amplitudes based on the well-established methodology of non-linear curve registration.
Methods: First, the to-be-analysed individual ABRs are temporally aligned - either with one another or, if available, with a pre-existing template - by locally compressing or stretching their time axes with smooth and invertible time warping functions. Then, the individual latencies and amplitudes of relevant ABR waves are obtained by picking the latencies of the waves' peaks and troughs on the common (aligned) time axis and combining these with the individual aligned responses and inverse time warping functions.
Results: Using an example ABR data set with a wide range of response latencies and signal-to-noise ratios (SNRs), we test different choices for fitting the time warping functions. We cross-validate the warping results using independent response replicates and compare automatically and manually extracted latencies and amplitudes for ABR waves I and V. Using a Bayesian approach, we show that, for the best registration condition, automatic and manual data were statistically similar.
Conclusions: Non-linear curve registration can be used to temporally align individual ABRs and extract their wave latencies and amplitudes in a way that closely matches results from manual picking.
Competing Interests: Declaration of Competing Interest None of the authors declares any conflict of interest.
(Copyright © 2020. Published by Elsevier B.V.)
Databáze: MEDLINE
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