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Related Concept Videos

Hearing

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.

Perception of Sound Waves

Perception of Sound Waves

The human ear is not equally sensitive to all frequencies in the audible range. It may perceive sound waves with the same pressure but different frequencies as having different loudness. Moreover, the perception of sound waves depends on the health of an individual's ears, which decays with age. The health of one's ears may also be affected by regular exposure to loud noises.
The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same frequency...

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Updated: Jun 5, 2026

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Data Acquisition and Analysis In Brainstem Evoked Response Audiometry In Mice

Published on: May 10, 2019

Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework.

Sichao Liang¹, Jia Xu¹, Haixu Liu²

¹Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.

Otolaryngology--Head and Neck Surgery : Official Journal of American Academy of Otolaryngology-Head and Neck Surgery

|June 1, 2024

Summary

Deep learning models, including Light-MLP and Wide&Deep, show high accuracy in recognizing auditory brainstem response (ABR) waveforms. These advanced frameworks improve ABR analysis for individuals of all ages and hearing abilities.

Keywords:

Wide&Deep model auditory brainstem response deep learning waveform automatic recognition

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Area of Science:

Audiology
Artificial Intelligence
Biomedical Engineering

Background:

Auditory Brainstem Response (ABR) waveform recognition is crucial for audiological assessment but can be challenging for certain populations.
Deep learning offers potential solutions for improving the accuracy and efficiency of ABR analysis.

Purpose of the Study:

To investigate the effectiveness of deep learning frameworks for automatic ABR waveform recognition.
To assess performance across diverse age groups and hearing loss levels.

Main Methods:

A descriptive study analyzed pure tone audiometry and ABR data from 100 participants.
Feature vectors were generated using time-domain, frequency-domain ABR signals, and demographic data.
An enhanced Wide&Deep model incorporating a Light-multi-layer perceptron (MLP) was used for ABR waveform recognition.

Main Results:

The Light-MLP model achieved a weighted average accuracy of 95.4%.
The Wide&Deep model achieved a weighted average accuracy of 91.0%.
Both models demonstrated high recognition accuracy across different participant groups.

Conclusions:

Deep learning models, specifically Light-MLP and Wide&Deep, show significant promise for accurate ABR waveform recognition.
The Light-MLP model outperformed the Wide&Deep model in this study.
Further data augmentation may enhance Wide&Deep model performance.