Machine learning identification of tinnitus-related features in auditory peripheral spontaneous activity in a guinea pig noise-induced tinnitus model.

Journal: Hearing research
Published Date: Jul 16, 2025

Abstract

OBJECTIVES: Tinnitus affects millions globally, yet its clinical assessment relies on subjective reports, limiting diagnostic accuracy and treatment development. This study aimed to identify objective, tinnitus-related features within ensemble spontaneous activity (ESA) recorded from the cochlear surface in a guinea pig model and to evaluate their reversibility using extracochlear electrical stimulation (EES) and machine learning.

Authors

  • Linhan Huang
    ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai 20031, China; NHC Key Laboratory of Hearing Medicine Research, Fudan University, Shanghai 20031, China.
  • Min Chen
    School of Computer Science and TechnologyHuazhong University of Science and Technology Wuhan 430074 China.
  • Shuwen Fan
    ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai 20031, China; NHC Key Laboratory of Hearing Medicine Research, Fudan University, Shanghai 20031, China.
  • Nafisa Tursun
    ENT Institute and Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai 20031, China; NHC Key Laboratory of Hearing Medicine Research, Fudan University, Shanghai 20031, China.
  • Xueying He
    College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China.
  • Wen Li
  • Shufeng Li
    Department of Urology, Stanford University School of Medicine, Palo Alto, California.

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