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Semin Hear 2022; 43(03): 240-250
DOI: 10.1055/s-0042-1756166
Review Article

Translational Applications of Machine Learning in Auditory Electrophysiology

Spencer Smith
1   Department of Speech, Language, and Hearing Sciences, University of Texas at Austin, Austin, Texas
› Author Affiliations FUNDING This work was supported in part by NIH grant #K01DC017192.
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Abstract

Machine learning (ML) is transforming nearly every aspect of modern life including medicine and its subfields, such as hearing science. This article presents a brief conceptual overview of selected ML approaches and describes how these techniques are being applied to outstanding problems in hearing science, with a particular focus on auditory evoked potentials (AEPs). Two vignettes are presented in which ML is used to analyze subcortical AEP data. The first vignette demonstrates how ML can be used to determine if auditory learning has influenced auditory neurophysiologic function. The second vignette demonstrates how ML analysis of AEPs may be useful in determining whether hearing devices are optimized for discriminating speech sounds.

Keywords

machine learning - auditory evoked potentials - otoacoustic emissions


Publication History

Article published online:
26 October 2022

© 2022. Thieme. All rights reserved.

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