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http://dx.doi.org/10.5370/KIEE.2018.67.7.962

Postoperative Pain Assessment based on Derivative Waveform of Photoplethysmogram  

Seok, Hyeon Seok (Dept. of Biomedical Engineering, Chonnam National University)
Shin, Hangsik (Dept. of Biomedical Engineering, Chonnam National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.67, no.7, 2018 , pp. 962-968 More about this Journal
Abstract
In this study, we developed novel indicators to assess postoperative pain based on PPG derivative waveform. As the candidate indicator of postoperative pain assessment, the time from the start of beating to the n-th peak($T_n$) and the n-th peak amplitude($A_n$) of the PPG derivative were selected. In order to verify derived indicators, each candidate indicator was derived from the PPG of 78 subjects before and after surgery, and it was confirmed whether significant changes were observed after surgery. Logistic classification was performed with each proposed indicator to calculate the pain classification accuracy, then the classification performance was compared with SPI(Surgical Pleth Index, GE Healthcare, Chicago, US). The results showed that there were significant differences(p < 0.01) in all indicators except for $T_3$ and $A_3$. The coefficient of variation(CV) of every time-related indicators were lower than the CV of SPI(30.43%), however, the CV in amplitude-related parameters were higher than that of SPI. Among the candidate indicators, amplitude of the first peak, $A_1$, showed that highest accuracy in post-operative pain classification, 68.72%, and it is 15.53% higher than SPI.
Keywords
PPG derivative; Photoplethysmography; Postoperative pain; Surgical pain;
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Times Cited By KSCI : 1  (Citation Analysis)
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