Anaesthesia monitoring using fuzzy logic

Baig, Mirza Mansoor, GholamHosseini, Hamid, Kouzani, Abbas and Harrison, Michael J. 2011, Anaesthesia monitoring using fuzzy logic, Journal of clinical monitoring and computing, vol. 25, no. 5, pp. 339-347.

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Title Anaesthesia monitoring using fuzzy logic
Author(s) Baig, Mirza Mansoor
GholamHosseini, Hamid
Kouzani, AbbasORCID iD for Kouzani, Abbas
Harrison, Michael J.
Journal name Journal of clinical monitoring and computing
Volume number 25
Issue number 5
Start page 339
End page 347
Total pages 9
Publisher Springer
Place of publication Amsterdam, The Netherlands
Publication date 2011
ISSN 1387-1307
Keyword(s) anaesthesia monitoring
fuzzy logic
hypovolaemia diagnosis
patient monitoring systems
Summary Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2011, Springer
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