A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.

Journal: Computers in biology and medicine
PMID: 37801923

Abstract

BACKGROUND: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryngeal movement disorders (LMDs), there are limited available databases to be investigated. Another worthy explored research question is which above sub-disorder benefits most from diagnosis based on sustained phonations. The question is from the fact that sustained phonations can help detect pathological voice from healthy voice.

Authors

  • Yu Yao
    Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, ‡School of Computer Science and Technology, and §Center of Information Support & Assurance Technology, Anhui University , Hefei, 230601 Anhui, China.
  • Maria Powell
    Vanderbilt University Medical Center, 1211 Medical Center Drive, Nashville, 37232, TN, USA.
  • Jules White
    Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
  • Jian Feng
    Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, People's Republic of China.
  • Quchen Fu
    The Department of Electrical Engineering and Computer Science, Vanderbilt University, 2301 Vanderbilt Place, Nashville, 37235, TN, USA.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Douglas C Schmidt
    Department of Computer Science, William & Mary, Williamsburg, VA, United States.

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