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Updated: Sep 17, 2025
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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
Published on: December 9, 2022
2.6K
An Open-Source Deep Learning-Based GUI Toolbox for Automated Auditory Brainstem Response Analyses (ABRA).
Abhijeeth Erra1, Jeffrey Chen1, Cayla M Miller2
1Data Institute, University of San Francisco, San Francisco, CA.
Research Square
|June 30, 2025
Summary
A new deep learning tool, Auditory Brainstem Response Analyzer (ABRA), automates hearing loss analysis. This innovation standardizes auditory brainstem response (ABR) measurements, improving accuracy and reproducibility in hearing research.
Area of Science:
- Neuroscience
- Bioengineering
- Computational Biology
Background:
- Hearing loss is a global health issue linked to cognitive decline.
- Auditory brainstem responses (ABRs) are crucial for assessing auditory pathway function.
- Current ABR analysis is subjective, limiting reproducibility.
Purpose of the Study:
- To develop an automated tool for analyzing ABR waveforms.
- To standardize ABR metric extraction for improved research consistency.
- To enhance the efficiency and reliability of hearing loss diagnostics.
Main Methods:
- Developed Auditory Brainstem Response Analyzer (ABRA), an open-source, deep learning-powered GUI.
- Utilized convolutional neural networks trained on diverse ABR datasets.
- Automated extraction of ABR metrics like peak amplitude, latency, and auditory thresholds.
Main Results:
- ABRA achieved performance comparable to expert human analysis.
- The tool significantly reduced ABR analysis time.
- Demonstrated enhanced reproducibility across multi-laboratory datasets.
Conclusions:
- ABRA offers automated, standardized ABR analysis, overcoming traditional limitations.
- The tool facilitates interdisciplinary insights in hearing research and neuroscience.
- ABRA is accessible online, promoting wider adoption and collaboration.
