Machine learning approaches used to analyze auditory evoked responses from the human auditory brainstem: A systematic review.

Journal: Computer methods and programs in biomedicine
PMID: 36122495

Abstract

BACKGROUND: The application of machine learning algorithms for assessing the auditory brainstem response has gained interest over recent years with a considerable number of publications in the literature. In this systematic review, we explore how machine learning has been used to develop algorithms to assess auditory brainstem responses. A clear and comprehensive overview is provided to allow clinicians and researchers to explore the domain and the potential translation to clinical care.

Authors

  • Hasitha Wimalarathna
    Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada; National Centre for Audiology, Western University, London, Ontario, Canada. Electronic address: hwimalar@uwo.ca.
  • Sangamanatha Ankmnal-Veeranna
    National Centre for Audiology, Western University, London, Ontario, Canada; College of Nursing and Health Professions, School of Speech and Hearing Sciences, The University of Southern Mississippi, J.B. George Building, Hattiesburg, MS, USA.
  • Chris Allan
    National Centre for Audiology, Western University, London, Ontario, Canada; School of Communication Sciences & Disorders, Western University, London, Ontario, Canada.
  • Sumit K Agrawal
  • Jagath Samarabandu
    Department of Electrical & Computer Engineering, Western University, London, Ontario, Canada.
  • Hanif M Ladak
  • Prudence Allen
    National Centre for Audiology, Western University, London, Ontario, Canada; School of Communication Sciences & Disorders, Western University, London, Ontario, Canada.

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