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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R

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A simple algorithm for objective threshold determination of auditory brainstem responses.

Kirupa Suthakar1, M Charles Liberman1

  • 1Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston, MA, 02114, USA; Department of Otolaryngology, Harvard Medical School, Boston, MA, 02115, USA.

Hearing Research
|August 23, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an automated algorithm for determining auditory brainstem response (ABR) thresholds, reducing user bias in auditory function assessments. The novel cross-covariance method provides reliable and standardized ABR threshold measurements.

Keywords:
ABRAlgorithmAuditoryAuditory Brainstem ResponseAutomaticCorrelationHearingSPLSound Pressure LevelThreshold

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Area of Science:

  • Neuroscience
  • Auditory Neuroscience
  • Biomedical Engineering

Background:

  • Auditory brainstem response (ABR) is crucial for assessing auditory function.
  • Current ABR threshold determination relies on visual inspection, introducing user bias and variability.
  • Standardization across studies and labs is needed for reliable auditory assessments.

Purpose of the Study:

  • To develop and validate an automated algorithm for determining ABR thresholds.
  • To eliminate user bias and standardize ABR threshold determination.
  • To improve the objectivity and reproducibility of auditory function assessments.

Main Methods:

  • Reanalyzed two mouse ABR waveform datasets using a normalized cross-covariance algorithm.
  • Fit correlation-coefficient vs. level data with sigmoidal and two-term power functions.
  • Interpolated thresholds at a criterion value of 0.35 correlation-coefficient, validated against visual inspection.

Main Results:

  • Algorithm-computed thresholds closely matched visual thresholds from two independent observers.
  • Qualitative and quantitative assessments confirmed the algorithm's success in emulating expert decisions.
  • The cross-covariance method demonstrated robustness across different datasets and conditions.

Conclusions:

  • Automated cross-covariance analysis provides an objective and reliable method for ABR threshold determination.
  • This algorithm standardizes auditory function assessment, reducing inter-observer variability.
  • The method is robust to variations in equipment and investigator differences, enhancing ABR analysis.