Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic

被引:20
作者
Jensen, EW
Nebot, A
Caminal, P
Henneberg, SW
机构
[1] Univ Politecn Catalunya, Dept ESAII, Ctr Rec Engn Biomed, E-08028 Barcelona, Spain
[2] Univ Politecn Catalunya, Dept Llenguatges & Sist Informat, Barcelona 08034, Spain
[3] Rigshosp, Dept Anaesthesia, DK-2100 Copenhagen, Denmark
关键词
brain; evoked potentials; anaesthetics gases; nitrous oxide; cardiovascular system; effects; model; fuzzy logic;
D O I
10.1093/bja/82.1.25
中图分类号
R614 [麻醉学];
学科分类号
100217 [麻醉学];
摘要
The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFI) TWO different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2(ET)), AEP and ISOFI were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2(ET), DAI Or ISOFI) that had the highest causal relation with the output variables (ISOFI and MAP). The variables with highest causal relation constitute the ISOFI and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.
引用
收藏
页码:25 / 32
页数:8
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