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Objective auditory brainstem response classification using machine learning

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posted on 2023-08-30, 16:02 authored by Richard M. McKearney, Robert C. Mackinnon
The objective of this study was to use machine learning in the form of a deep neural network to objectively classify paired auditory brainstem response waveforms into either: ‘clear response’, ‘inconclusive’ or ‘response absent’. A deep convolutional neural network was constructed and fine-tuned using stratified 10-fold cross-validation on 190 paired ABR waveforms. The final model was evaluated on a test set of 42 paired waveforms. The full dataset comprised 232 paired ABR waveforms recorded from eight normal-hearing individuals. The dataset was obtained from the PhysioBank database. The paired waveforms were independently labelled by two audiological scientists in order to train the network and evaluate its performance. The trained neural network was able to classify paired ABR waveforms with 92.9% accuracy. The sensitivity and the specificity were 92.9% and 96.4%, respectively. This neural network may have clinical utility in assisting clinicians with waveform classification for the purpose of hearing threshold estimation. Further evaluation using a large clinically obtained dataset would provide further validation with regard to the clinical potential of the neural network in diagnostic adult testing, newborn testing and in automated newborn hearing screening.

History

Refereed

  • Yes

Volume

58

Issue number

4

Page range

224-230

Publication title

International Journal of Audiology

ISSN

1708-8186

Publisher

Taylor & Francis

File version

  • Accepted version

Language

  • eng

Legacy posted date

2019-02-13

Legacy creation date

2019-02-13

Legacy Faculty/School/Department

Faculty of Science & Engineering

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