title: Detection and classification of hypovolaemia during anaesthesia.
abstract: In recent years, there has been a rapid growth in patient monitoring and medical data analysis using decision support systems, smart alarm monitoring, expert systems and many other computer aided protocols. The main goal of this study was to enhance the developed diagnostic alarm system for detecting critical events during anaesthesia. The proposed diagnostic alarm system is called Fuzzy logic monitoring system-2 (FLMS-2). The performance of the system was validated through a series of off-line tests. When detecting hypovolaemia a substantial level of agreement was observed between FLMS-2 and the human expert and it is shown that system has a better performance with sensitivity of 94%, specificity of 90% and predictability of 72%.
authors.affiliation: School of Engineering, Auckland University of Technology, Auckland-1142, New Zealand. mirza.baig@aut.ac.nz
authors.forename: Mirza Mansoor
authors.initials: MM
authors.lastname: Baig
authors.forename: Hamid
authors.initials: H
authors.lastname: GholamHosseini
authors.forename: Si-Woong
authors.initials: SW
authors.lastname: Lee
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Harrison
mesh_terms: D000758:Anesthesia / Q000379:methods*; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D020896:Hypovolemia / Q000175:diagnosis* / Q000503:physiopathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity