A hierarchical system of on-line advisory for monitoring and controlling the depth of anaesthesia using self-organizing fuzzy logic |
| |
| Affiliation: | 1. Department of Mechanical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung-Li, TaoYuan, 320, Taiwan, R.O.C.;2. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield S1 3JD, UK;3. Department of Anaesthesia, The University of Glasgow, Western Infirmary, Dumbarton Road, Glasgow G11 6NT, UK;1. Division of Cardiology, The Ohio State University Medical Center, Columbus, Ohio;2. Cardiac Transplant Program, The Ohio State University Medical Center, Columbus, Ohio;3. Department of Internal Medicine, The Ohio State University, Columbus, Ohio;4. The Ohio State University College of Medicine, Columbus, Ohio;5. The Ohio State University College of Public Health, Columbus, Ohio;6. Division of Epidemiology, The Ohio State University College of Public Health, Columbus, Ohio;2. Texas Heart Institute, Houston, Texas;1. Division of Pediatric Cardiology, Arkansas Children''s Hospital, University of Arkansas for Medical Sciences, Little Rock, Arkansas;2. Biostatistics Shared Resource, Nationwide Children''s Hospital, Columbus, Ohio;3. Division of Pediatric Cardiology, Nationwide Children''s Hospital, Columbus, Ohio;4. Department of Anesthesiology and Pain Medicine, Nationwide Children''s Hospital, Columbus, Ohio;1. School of Information Science and Engineering, Shandong University, Jinan 250100, China;2. School of Information and Electronics Engineering, Shandong Institute of Business and Technology, Yantai 264005, China;3. Suzhou Institute, Shandong University, Suzhou 215123, China |
| |
| Abstract: | A hierarchical system has been developed to on-line advise on the concentration of inhaled volatile anaesthetics for controlling depth of anaesthesia. It merges on-line measurements (such as systolic arterial pressure and heart rate) and clinical information (such as sweating, lacrimation and movement) using a hierarchical architecture and self-organizing fuzzy logic for reasoning. It has been developed to predict depth of anaesthesia from either a “hand-crafted” anaesthetists’ or machine-learning rule-base using self-organizing learning system and control the drug levels using self-organizing fuzzy logic algorithm. In this paper, machine-learning rule-base has been validated via tests with 10 patients off-line and 17 patients on-line. The drug controller rule-base has also been validated via pre-tuning on 10 off-line patients and testing on 17 on-line patients. After extensive validation of this system, this on-line approach has shown promise and very successful for reducing the recovery time in comparison with either 10 patients off-line or other research. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
