Objective auditory brainstem response classification using machine learning

Abstract

Objective: 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’.

Design: 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.

Study Sample: 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.

Results: 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.

Conclusions: 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.

Keywords:

• Auditory Brainstem Evoked Response
• classification
• supervised machine learning
• neural network models

Acknowledgements

The authors are grateful to Ikaro Silva and Michael Epstein for contributing their ABR dataset to the PhysioBank database. The authors would also like to thank Paul Solomon for his enthusiasm and advice regarding machine learning.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID details

Richard M. McKearney: ORCID iD 0000-0001-7030-5617

Robert C. MacKinnon: ORCID iD 0000-0002-6486-5578

Additional information

Notes on contributors

Richard M. McKearney

R. McKearney designed the study, constructed the neural network, performed the computational experiments, analysed the data and drafted the manuscript. R. MacKinnon independently labelled the ABR waveforms, assisted with data analysis, added to and critically appraised the manuscript.

Robert C. MacKinnon

R. McKearney designed the study, constructed the neural network, performed the computational experiments, analysed the data and drafted the manuscript. R. MacKinnon independently labelled the ABR waveforms, assisted with data analysis, added to and critically appraised the manuscript.