ArticleComputer methods and programs in biomedicine2020
Automated extraction of auditory brainstem response latencies and amplitudes by means of non-linear curve registration.
Article in Computer methods and programs in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- White Matter Changes in Cases with Unilateral Idiopathic Sudden Sensorineural Hearing Loss Indicated by Diffusion Tensor Imaging Based on Tract-Based Spatial Statistics.The journal of international advanced otology · 2025Article
- Development and Evaluation of Automated Tools for Auditory-Brainstem and Middle-Auditory Evoked Potentials Waves Detection and Annotation.Brain sciences · 2022Article
- Could Tailored Chirp Stimuli Benefit Measurement of the Supra-threshold Auditory Brainstem Wave-I Response?Journal of the Association for Research in Otolaryngology : JARO · 2022Article
- Optimizing non-invasive functional markers for cochlear deafferentation based on electrocochleography and auditory brainstem responses.The Journal of the Acoustical Society of America · 2022Article
- Arc-Length Re-Parametrization and Signal Registration to Determine a Characteristic Average and Statistical Response Corridors of Biomechanical Data.Frontiers in bioengineering and biotechnology · 2022Article
- Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term Memory.Frontiers in medicine · 2020Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
BACKGROUND AND
objectivesAnimal 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.
methodsFirst, 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.
resultsUsing 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.
conclusionsNon-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.
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