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Related Experiment Video

Updated: Nov 19, 2025

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

Published on: January 23, 2017

Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory

Hasitha Wimalarathna¹, Sangamanatha Ankmnal-Veeranna², Chris Allan³

¹Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada.

Computer Methods and Programs in Biomedicine

|January 30, 2021

Summary

Machine learning models can automate auditory brainstem response (ABR) analysis for children with listening difficulties. The Xgboost algorithm achieved 92% accuracy, offering a potential clinical tool for audiologists.

Keywords:

Auditory Brainstem Responses Auditory Processing Disorder Machine Learning Signal feature extraction

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Last Updated: Nov 19, 2025

Behavioral Assessment of Hearing in 2 to 4 Year-old Children: A Two-interval, Observer-based Procedure Using Conditioned Play-based Responses

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Published on: January 23, 2017

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Quantitative Assessment of Cortical Auditory-tactile Processing in Children with Disabilities

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Published on: January 29, 2014

Area of Science:

Neuroscience
Biomedical Engineering
Machine Learning

Background:

Auditory brainstem responses (ABRs) assess auditory nervous system integrity in individuals with listening difficulties.
Manual ABR interpretation by audiologists requires expertise and can be prone to error.
Machine learning (ML) offers a promising approach to automate ABR analysis and enhance diagnostic accuracy.

Purpose of the Study:

To identify a suitable ML technique for automating ABR analysis within the Auditory Processing Disorder (APD) clinical test battery.
To reduce human error and improve the efficiency of ABR interpretation in clinical settings.

Main Methods:

Analyzed ABR responses from 136 children evaluated for auditory processing difficulties.
Applied various ML algorithms including SVM, RF, DT, GB, Xgboost, and NN.
Utilized signal feature extraction techniques and statistical testing to determine the most robust model.

Main Results:

Identified clinically significant time-frequency signal features.
The Xgboost ML model demonstrated the highest robustness and accuracy at 92%.
This model effectively identified neurological abnormalities in ABR waveforms.

Conclusions:

Accurate ML models can automate suprathreshold ABR waveform analysis.
This research is expected to lead to an ML-based screening tool for audiologists.
The findings may guide future development of ML paradigms to enhance clinical audiology test batteries.