ABRpresto: An algorithm for automatic thresholding of the Auditory Brainstem Response using resampled...

Area of Science:

• Neuroscience
• Audiology
• Biomedical Engineering

Background:

• Auditory Brainstem Response (ABR) is crucial for assessing auditory pathway health.
• Estimating ABR threshold (lowest response level) is challenging due to waveform shifts and low signal-to-noise ratio.
• Current clinical practice relies on subjective visual inspection of ABR waveforms.

Purpose of the Study:

• To develop and validate an objective, automated algorithm for ABR threshold estimation.
• To improve the accuracy and efficiency of ABR threshold determination in preclinical research.

Main Methods:

• An algorithm was developed using cross-correlation of two independent response averages for each stimulus level.
• Responses were randomly split, median waveforms computed, and normalized cross-correlation analyzed.
• Resampling and fitting distributions with sigmoid or power law functions determined threshold values.

Main Results:

• The algorithm achieved 92% accuracy within ±10 dB of human-rated thresholds on mouse data.
• Performance surpassed several previously published algorithms on the same dataset.
• The algorithm has successfully replaced manual ABR threshold estimation in preclinical studies.

Conclusions:

• The developed cross-correlation algorithm provides a robust and accurate method for ABR threshold estimation.
• This automated approach significantly enhances objectivity and saves time in auditory research.
• The algorithm represents a valuable advancement for preclinical auditory assessments.