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심전도를 이용한 통증자각 패턴분류기 설계

Other Titles
 Design of a Pattern Classifier for Pain Awareness using Electrocardiogram 
 임현준,  ;  유선국 
 Journal of Korea Multimedia Society (멀티미디어학회논문지), Vol.20(9) : 1509-1518, 2017 
Journal Title
Journal of Korea Multimedia Society(멀티미디어학회논문지)
Issue Date
Bio-Signal ; Electrocardiogram ; Pain ; Artificial Neural Network ; Support Vector Machine
Although several methods have been used to assess the pain levels, few practical methods for classifying presence or absence of the pain using pattern classifiers have been suggested. The aim of this study is to design an pattern classifier that classifies the presence or absence of the pain using electrocardiogram (ECG). We measured the ECG signal from 10 subjects with the painless state and the pain state(Induced by mechanical stimulation). The 10 features of heart rate variability (HRV) were extracted from ECG - MeanRRI, SDNN, rMSSD, NN50, pNN50 in the time domain; VLF, LF, HF, Total Power, LF/HF in the frequency domain; and we used the features as input vector of the pattern classifier's artificial neural network (ANN) / support vector machine (SVM) for classifying the presence or absence of the pain. The study results showed that the classifiers using ANN / SVM could classify the presence or absence of the pain with accuracies of 81.58% / 81.84%. The proposed classifiers can be applied to the objective assessment of pain level.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Yoo, Sun Kook(유선국) ORCID logo https://orcid.org/0000-0002-6032-4686
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