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Related Experiment Video

Updated: Aug 16, 2025

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Objective hearing threshold identification from auditory brainstem response measurements using supervised and

Dominik Thalmeier1,2, Gregor Miller3, Elida Schneltzer3

  • 1Institute of Computational Biology, Helmholtz Zentrum München, München, Germany.

BMC Neuroscience
|December 27, 2022
PubMed
Summary
This summary is machine-generated.

Automated methods for hearing threshold detection in mice improve speed and reduce bias. These tools aid in identifying genes related to hearing impairment in large-scale studies.

Keywords:
Auditory brainstem responseAutomationEvoked potentialsHigh-throughput hearing screeningObjective hearing threshold detection

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Area of Science:

  • Genetics
  • Neuroscience
  • Auditory Science

Background:

  • Hearing loss is a significant global health issue with considerable psychological impact.
  • Mouse models are crucial for understanding the genetic and pathophysiological mechanisms of hearing impairment.
  • Auditory phenotyping, particularly using auditory brainstem response (ABR), generates large datasets in mouse studies.

Purpose of the Study:

  • To develop and compare automated methods for hearing threshold identification from averaged ABR data.
  • To improve the efficiency, objectivity, and reproducibility of auditory phenotyping in high-throughput screening.
  • To facilitate the identification of candidate genes associated with hearing disorders.

Main Methods:

  • Developed and evaluated two automated approaches: a supervised method using neural networks and a self-supervised method combining signal power spectrum analysis, random forest, and curve fitting.
  • Trained neural networks on human-annotated ABR data for supervised learning.
  • Utilized signal processing and machine learning algorithms for self-supervised threshold detection.

Main Results:

  • Both automated methods demonstrated high performance in accurately and reliably detecting hearing thresholds.
  • The developed methods provide fast, unbiased identification and quality control of ABR data.
  • The approaches are suitable for integration into automated screening pipelines for genetic studies of hearing.

Conclusions:

  • Automated hearing threshold detection significantly enhances the efficiency and objectivity of auditory phenotyping in mouse models.
  • These methods offer a robust solution for large-scale genetic screening programs aimed at understanding hearing loss.
  • Freely available code and data promote reproducibility and further research in the field.