Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/77830, first published .
Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study

Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study

Enhancing Anesthetic Depth Assessment via Unsupervised Machine Learning in Processed Electroencephalography Analysis: Novel Methodological Study

Po-Yu Huang   1 , MD ;   Wei-Lun Hong   2 , MS ;   Hui-Zen Hee   3 , MD ;   Wen-Kuei Chang   1 , MD ;   Ching-Hung Lee   2, 4 , PhD ;   Chien-Kun Ting   1, 5 , MD, PhD

1 Department of Anesthesiology, Taipei Veterans General Hospital, Taipei City, Taiwan

2 Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu City, Taiwan

3 Department of Anesthesiology, Far Eastern Memorial Hospital, New Taipei City, Taiwan

4 Department of Electrical Engineering, Chung Yuan Christian University, Taoyuan City, Taiwan

5 Institute of Emergency and Critical Care Medicine, National Yang Ming Chiao Tung University, Taipei City, Taiwan

Corresponding Author:

  • Chien-Kun Ting, MD, PhD
  • Department of Anesthesiology
  • Taipei Veterans General Hospital
  • No. 201, Sec 2, Shipai Rd, Beitou District
  • Taipei City 11217
  • Taiwan
  • Phone: 886 228757549
  • Email: ckting2@gmail.com