An automated ICU agitation monitoring system for video streaming using deep learning classification.

Journal: BMC medical informatics and decision making
PMID: 38500135

Abstract

OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning.

Authors

  • Pei-Yu Dai
    Department of Computer Science, Tunghai University, Taichung, Taiwan.
  • Yu-Cheng Wu
    Department of Critical Care Medicine, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Ruey-Kai Sheu
    Department of Computer Science, Tunghai University, No. 1727, Section 4, Taiwan Blvd, Xitun District, Taichung 407224, Taiwan.
  • Chieh-Liang Wu
    Center for Quality Management, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Shu-Fang Liu
    Supervisor of Nursing Department, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Pei-Yi Lin
    Department of Emergency, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong 510260, China.
  • Wei-Lin Cheng
    Department of Computer Science, Tunghai University, Taichung, Taiwan.
  • Guan-Yin Lin
    Department of Nursing, Taichung Veterans General Hospital, Taichung, Taiwan.
  • Huang-Chien Chung
    Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung, Taiwan.
  • Lun-Chi Chen
    College of Engineering, Tunghai University, Taichung, Taiwan.

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