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Evaluation of Auditory Brainstem Response in Chicken Hatchlings
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Objective auditory brainstem response classification using machine learning.

Richard M McKearney1, Robert C MacKinnon2

  • 1a Department of Audiology , Guy's and St Thomas' NHS Foundation Trust , London , UK.

International Journal of Audiology
|January 22, 2019
PubMed
Summary
This summary is machine-generated.

A deep neural network accurately classified auditory brainstem response (ABR) waveforms, aiding in hearing threshold estimation. This machine learning approach shows promise for objective ABR analysis in clinical settings.

Keywords:
Auditory Brainstem Evoked Responseclassificationneural network modelssupervised machine learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Audiology

Background:

  • Auditory Brainstem Response (ABR) waveform classification is crucial for hearing threshold estimation.
  • Objective and accurate classification of ABR waveforms can be challenging for clinicians.

Purpose of the Study:

  • To develop and evaluate a deep neural network for objective classification of paired ABR waveforms.
  • To categorize ABR waveforms into 'clear response', 'inconclusive', or 'response absent' categories.

Main Methods:

  • A deep convolutional neural network was designed and trained on 190 paired ABR waveforms.
  • Stratified 10-fold cross-validation was employed for model fine-tuning.
  • The model's performance was assessed on an independent test set of 42 paired waveforms.

Main Results:

  • The neural network achieved 92.9% accuracy in classifying paired ABR waveforms.
  • The model demonstrated high sensitivity (92.9%) and specificity (96.4%).

Conclusions:

  • The developed neural network shows potential clinical utility in assisting with ABR waveform classification for hearing threshold estimation.
  • Further validation with larger clinical datasets is recommended to explore its potential in diagnostic and screening applications.