A Novel Continuous Visual Analog Scale Model Derived from Pain-relief Demand Index via Hilbert Huang Transform for Postoperative Pain

NCUIR > Research Center > Research Center for adaptive Data analysis > journal & Dissertation > Item 987654321/50656

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50656

Title:	A Novel Continuous Visual Analog Scale Model Derived from Pain-relief Demand Index via Hilbert Huang Transform for Postoperative Pain
Authors:	Yeh,JR;Sun,WZ;Wen,YR;Shieh,JS
Contributors:	數據分析方法研究中心
Keywords:	RATING-SCALES
Date:	2011
Issue Date:	2012-03-27 17:50:23 (UTC+8)
Publisher:	國立中央大學
Abstract:	In this investigation, we proposed a continuous pain intensity scaling model derived from an innovative parameter of pain-relief demand (PRD) index for acute postoperative patients of gynecology using patient-controlled analgesia (PCA) via the occurrence frequency of pain-relief demands. To derive PRD, a simplified fluctuation time series of pain feeling was simulated according to the distribution of pain-relief demands using an exponential decay function. Furthermore, we applied the Hilbert Huang transform to obtain the time-amplitude distribution of the first intrinsic mode function (IMF) of fluctuation time series and defined the PRD index using a function of amplitude. A total of 2466 visual analogue scale (VAS) values derived from interviews with 470 postoperative patients at various time points were compared with this PRD index extracted from the PCA records of the same patients. According to the statistical result of one-way analysis of variance (ANOVA, P < 0.001), VAS was significantly linearly dependent on the PRD index. Although the result of this VAS model is not a perfect linear relationship between VAS and PRD index (r = 0.490), it is significantly better than the result of our previous study using a novel fuzzy pain demand index (FPD). This result shows this approach is promising for further study.
Relation:	JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING
Appears in Collections:	[Research Center for adaptive Data analysis ] journal & Dissertation

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