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Auditory brainstem response classification: a hybrid model using time and frequency features.

Robert Davey1, Paul McCullagh, Gaye Lightbody

  • 1Department of Language and Communication Science, City University, Northampton Square, London EC1V 0HB, UK.

Artificial Intelligence in Medicine
|August 26, 2006
PubMed
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This study developed automated software to detect auditory brainstem response (ABR) waveforms, improving diagnostic objectivity. The hybrid model achieved high accuracy, assisting audiologists in hearing assessments.

Area of Science:

  • Audiology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Auditory Brainstem Response (ABR) analysis is crucial for hearing threshold determination and diagnosing neurological conditions.
  • Current ABR interpretation by audiologists is subjective and requires significant expertise.
  • Automating ABR detection can enhance objectivity and consistency in audiological assessments.

Purpose of the Study:

  • To develop and validate software classification models for automated ABR waveform detection.
  • To provide audiologists with an objective and consistent tool for ABR analysis.
  • To improve the accuracy and reliability of ABR interpretation.

Main Methods:

  • A dataset of 550 ABR waveforms from 85 subjects was used, with expert classifications.

Related Experiment Videos

  • Software models were created using time, frequency, and cross-correlation measures.
  • Artificial neural networks (NNs) and C5.0 decision tree algorithms were employed, with validation via cross-validation and data randomization.

    • Main Results:

    • A two-stage classification process was developed.
    • The first stage achieved 95.6% accuracy in classifying strong responses using time and frequency domain power measures.
    • The second stage, a hybrid model combining classifiers with the Dempster-Shafer method, reached 85% accuracy.

    Conclusions:

    • A hybrid software system was created, emulating audiologist ABR analysis.
    • The system integrates power, frequency analysis, and subaverage consistency for robust artifact handling.
    • This approach enhances classification accuracy and provides a more objective ABR detection method.