id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-308219-97gor71p	Elzeiny, Sami	Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images	2020-09-17		.txt	text/plain	5697	312	52	By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models' accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models. In this study, a new stress classification approach is proposed to classify the individual stress state into stressed or non-stressed by converting spatial images of inter-beat intervals of a PPG signal to frequency domain images and we use these pictures to train several CNN models.	./cache/cord-308219-97gor71p.txt	./txt/cord-308219-97gor71p.txt