Real-time threshold determination of auditory brainstem responses by cross-correlation analysis

Area of Science:

• Neuroscience
• Audiology
• Biomedical Engineering

Background:

• Auditory brainstem response (ABR) is crucial for objective hearing function assessment.
• Current ABR threshold determination relies on manual visual inspection, lacking automation and consistency.
• Automating ABR threshold determination is a long-standing challenge in audiology.

Purpose of the Study:

• To develop a robust, fully automated procedure for determining ABR thresholds.
• To enable reliable ABR testing in both animal models and human subjects.
• To overcome the limitations of current machine algorithms in ABR analysis.

Main Methods:

• Implemented a cross-correlation analysis to detect time-locked waveforms in ABR recordings.
• Automated the termination of level averaging based on waveform detection.
• Defined threshold level by a significant increase in sweep numbers for qualified averaging.
• Adapted level averaging based on signal-to-noise ratio using cross-correlation.

Main Results:

• Achieved a strong agreement between the automated algorithm's results and human expert readings.
• Demonstrated the method's effectiveness in both animal and human ABR tests.
• The algorithm successfully adapted to varying signal-to-noise ratios.

Conclusions:

• The developed method provides a robust and fully automated approach to ABR threshold determination.
• This automation enhances the efficiency and reliability of hearing function assessments.
• The technique holds promise for widespread application in clinical and research settings.