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Related Experiment Video

Updated: Nov 19, 2025

Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Automatic Recognition of Auditory Brainstem Response Characteristic Waveform Based on Bidirectional Long Short-Term

Cheng Chen1, Li Zhan2, Xiaoxin Pan1

  • 1School of Computer and Communication Engineering, University of Science & Technology Beijing, Beijing, China.

Frontiers in Medicine
|January 28, 2021
PubMed
Summary

This study introduces an automated method for analyzing auditory brainstem response (ABR) waveforms using a BiLSTM machine learning model. The technique significantly reduces analysis time and improves diagnostic accuracy for hearing dysfunction.

Keywords:
auditory brainstem responsebi-directional long short-term memorycharacteristic waveform recognitionneural network modelwavelet transform

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Auditory brainstem response (ABR) testing is crucial for diagnosing hearing dysfunction.
  • Current ABR analysis relies on manual interpretation, which is time-consuming and subjective.
  • Objective and efficient ABR analysis is needed for clinical practice.

Purpose of the Study:

  • To develop an automated method for ABR waveform recognition.
  • To improve the efficiency and accuracy of ABR data analysis.
  • To reduce the burden on clinicians in diagnosing hearing impairments.

Main Methods:

  • Human ABR data was recorded and preprocessed using binarization.
  • A bidirectional long short-term memory (BiLSTM) network was developed for ABR sampling point classification.
  • Thresholding was applied to obtain mark points for waveform analysis.

Main Results:

  • The BiLSTM network achieved a recognition accuracy of 92.91% on 614 clinical ABR datasets.
  • The average detection time per dataset was reduced to 0.05 seconds.
  • The method demonstrated good noise resistance and specific network parameter exploration.

Conclusions:

  • The proposed BiLSTM-based machine learning technique enables automatic recognition of ABR waveforms.
  • This automated approach can significantly reduce recording time and aid clinical diagnosis.
  • The method shows strong potential for future clinical application in hearing diagnostics.