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一种改进的去趋势移动平均法用于监测麻醉深度。

An improved detrended moving-average method for monitoring the depth of anesthesia.

机构信息

University of Southern Queensland, Toowoomba, Qld. 4350, Australia.

出版信息

IEEE Trans Biomed Eng. 2010 Oct;57(10):2369-78. doi: 10.1109/TBME.2010.2053929. Epub 2010 Jun 28.

DOI:10.1109/TBME.2010.2053929
PMID:20595085
Abstract

The detrended moving-average (DMA) method is a new approach to quantify correlation properties in nonstationary signals with underlying trends. This paper monitored the depth of anesthesia (DoA) using modified DMA (MDMA) method. MDMA provides a power-law relation between the fluctuation function F(MDMA)(s) and the scale s: F(MDMA)(s)αs(α), where α is the slope of F(MDMA)(s) in the logarithm scale. We applied the MDMA to monitor the DoA by computing the scaling exponent F(α) and F(min) values. To validate the proposed method, we compared our results with the bispectral index (BIS) monitor. We found a close correlation between our results and BIS with r(F (min)) = 0.9346, r(2)(F(min)) = 0.9183, and r(F(α)) = 0.9458, r(2)(F(α)) = 0.8855. Our method reflects the state of consciousness of a patient undergoing general anesthesia faster than BIS as observed clinically. The minimum time delay between the BIS and F(min) trends was 12 s and the maximum was 178 s. Furthermore, in the case of poor signal quality, our results agreed with clinical observation, which indicates that our method can accurately estimate a patient's hypnotic state in such circumstances. F(α) and F(min) trends are responsive and their movement seems similar to changes in the clinical state of the patients.

摘要

去趋势移动平均(DMA)方法是一种新的方法,可以量化具有潜在趋势的非平稳信号中的相关特性。本文使用改进的 DMA(MDMA)方法监测麻醉深度(DoA)。MDMA 在波动函数 F(MDMA)(s) 和标度 s 之间提供幂律关系:F(MDMA)(s)αs(α),其中α是 F(MDMA)(s)在对数标度上的斜率。我们通过计算标度指数 F(α)和 F(min)值应用 MDMA 来监测 DoA。为了验证所提出的方法,我们将结果与双谱指数(BIS)监测仪进行了比较。我们发现我们的结果与 BIS 密切相关,r(F (min)) = 0.9346,r(2)(F(min)) = 0.9183,r(F(α)) = 0.9458,r(2)(F(α)) = 0.8855。我们的方法比 BIS 更快地反映接受全身麻醉的患者的意识状态,临床上观察到的。BIS 和 F(min)趋势之间的最小时间延迟为 12 秒,最大为 178 秒。此外,在信号质量较差的情况下,我们的结果与临床观察一致,这表明我们的方法可以在这种情况下准确估计患者的催眠状态。F(α)和 F(min)趋势具有响应性,它们的运动似乎与患者临床状态的变化相似。

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