%0 Journal Article
	%A Chi Jung Kim and  Mincheol Whang and  Eui Chul Lee
	%D 2011
	%J International Journal of Psychological and Behavioral Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 59, 2011
	%T Identification of Arousal and Relaxation by using SVM-Based Fusion of PPG Features
	%U https://publications.waset.org/pdf/10837
	%V 59
	%X In this paper, we propose a new method to distinguish
between arousal and relaxation states by using multiple features
acquired from a photoplethysmogram (PPG) and support vector
machine (SVM). To induce arousal and relaxation states in subjects, 2
kinds of sound stimuli are used, and their corresponding biosignals are
obtained using the PPG sensor. Two features–pulse to pulse interval
(PPI) and pulse amplitude (PA)–are extracted from acquired PPG
data, and a nonlinear classification between arousal and relaxation is
performed using SVM.
This methodology has several advantages when compared with
previous similar studies. Firstly, we extracted 2 separate features from
PPG, i.e., PPI and PA. Secondly, in order to improve the classification
accuracy, SVM-based nonlinear classification was performed.
Thirdly, to solve classification problems caused by generalized
features of whole subjects, we defined each threshold according to
individual features.
Experimental results showed that the average classification
accuracy was 74.67%. Also, the proposed method showed the better
identification performance than the single feature based methods.
From this result, we confirmed that arousal and relaxation can be
classified using SVM and PPG features.
	%P 588 - 592