Machine Learning-Based Diagnosis of Chronic Subjective Tinnitus With Altered Cognitive Function: An Event-Related Potential Study.

Journal: Ear and hearing
PMID: 40232877

Abstract

OBJECTIVES: Due to the absence of objective diagnostic criteria, tinnitus diagnosis primarily relies on subjective assessments. However, its neuropathological features can be objectively quantified using electroencephalography (EEG). Despite the existing research, the pathophysiology of tinnitus remains unclear. The objective of this study was to gain a deeper comprehension of the neural mechanisms underlying tinnitus through the comparison of cognitive event-related potentials in patients with tinnitus and healthy controls (HCs). Furthermore, we explored the potential of EEG-derived features as biomarkers for tinnitus using machine learning techniques.

Authors

  • Jihoo Kim
    Department of Chemistry, KAIST, Daejeon, 34141, Korea.
  • Kang Hyeon Lim
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Republic of Korea.
  • Euijin Kim
    Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea.
  • Seunghu Kim
    Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea.
  • Hong Jin Kim
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Republic of Korea.
  • Ye Hwan Lee
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Republic of Korea.
  • Sungkean Kim
    Department of Applied Artificial Intelligence, Hanyang University, Ansan, Republic of Korea; Department of Human-Computer Interaction, Hanyang University, Ansan, Republic of Korea. Electronic address: kimsk@hanyang.ac.kr.
  • June Choi
    Department of Otorhinolaryngology-Head and Neck Surgery, Ansan Hospital, Korea University College of Medicine, Republic of Korea.

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