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A comparison of supervised classification methods for auditory brainstem response determination.

Paul McCullagh¹, Haiying Wang, Huiru Zheng

¹Department of Computing and Mathematics, University of Ulster, United Kingdom.

Studies in Health Technology and Informatics

|October 4, 2007

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Machine learning accurately classifies auditory brainstem response (ABR) data for hearing loss assessment. Naïve Bayes achieved 83.4% accuracy, demonstrating AI

Area of Science:

Audiology
Biomedical Engineering
Machine Learning

Background:

Auditory Brainstem Response (ABR) is crucial for hearing loss quantification.
ABR interpretation is subjective and relies on expert experience.

Purpose of the Study:

Investigate machine learning for objective ABR pattern classification.
Develop automated ABR analysis to aid clinical diagnosis.

Main Methods:

Extracted features from 550 ABR waveforms across 85 subjects.
Compared Naïve Bayes, Support Vector Machine, Multi-Layer Perceptron, and KStar classifiers.
Utilized time and wavelet domain features for classification.

Main Results:

Naïve Bayes achieved the highest classification accuracy (83.4%) with five features.

Related Experiment Videos

Naïve Bayes demonstrated the highest specificity (86.3%).

Support Vector Machine models yielded the highest sensitivity (93.1%).

Conclusions:

Selected features and machine learning models show relevance for ABR analysis.
Machine learning offers a feasible approach to objective ABR interpretation.
AI-driven classification can enhance audiology diagnostics.