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A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity

Mustafa Küçükakarsu1, Ahmet Reşit Kavsaoğlu1, Fayadh Alenezi2

  • 1Department of Biomedical Engineering, Faculty of Engineering, Karabuk University, Karabuk 78050, Turkey.

Diagnostics (Basel, Switzerland)
|February 11, 2023
PubMed
Summary
This summary is machine-generated.

This study demonstrates that machine learning can autonomously conduct hearing tests using electro-encephalography (EEG) signals. The Light Gradient Boosting Machine (LGBM) algorithm achieved 84% accuracy in predicting audibility from brainwaves.

Keywords:
EEGaudiometryautomatic audiometric systembrain signalsmachine learning

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Standard audiometry relies on patient responses, which can be subjective.
  • Electro-encephalography (EEG) records brain's electrical activity, offering an objective measure.
  • Automating hearing tests with machine learning could improve efficiency and accessibility.

Purpose of the Study:

  • To develop and validate a machine learning model for autonomous hearing tests using EEG signals.
  • To investigate the efficacy of various machine learning algorithms in classifying heard versus unheard sounds based on EEG data.
  • To determine the optimal machine learning algorithm for this application.

Main Methods:

  • EEG signals were recorded from participants exposed to sounds of varying amplitudes and wavelengths.
  • MATLAB was used for stimulus presentation and response recording, while Python was employed for data analysis.
  • Machine learning algorithms including Naïve Bayes, LGBM, SVM, decision tree, k-NN, logistic regression, and random forest were applied after EEG data pre-processing and feature extraction (TF-IDF).

Main Results:

  • The Light Gradient Boosting Machine (LGBM) algorithm demonstrated the highest performance in classifying audibility.
  • The LGBM algorithm achieved an 84% success rate in predicting whether a participant heard a given sound.
  • EEG signal analysis using TF-IDF and Count Vectorizer effectively identified relevant features for classification.

Conclusions:

  • Machine learning, particularly the LGBM algorithm, shows significant potential for conducting autonomous hearing tests via EEG.
  • This approach offers a promising, objective alternative or supplement to traditional audiometry.
  • Further research may be needed to integrate this technology into clinical practice, potentially with audiologist oversight.