Research Article
A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality
@ARTICLE{10.4108/icst.pervasivehealth.2015.259248, author={John Gialelis and Chris Panagiotou and Ioakeim Samaras and Petros Chondros and Dimitris Karadimas}, title={A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={1}, number={4}, publisher={EAI}, journal_a={PHAT}, year={2015}, month={8}, keywords={eeg, sleep stages, svm, fis}, doi={10.4108/icst.pervasivehealth.2015.259248} }- John Gialelis
Chris Panagiotou
Ioakeim Samaras
Petros Chondros
Dimitris Karadimas
Year: 2015
A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality
PHAT
EAI
DOI: 10.4108/icst.pervasivehealth.2015.259248
Abstract
This paper compares two supervised learning algorithms for predicting the sleep stages based on the human brain activity. The first step of the presented work regards feature extraction from real human electroencephalography (EEG) data together with its corresponding sleep stages that are utilized for training a support vector machine (SVM), and a fuzzy inference system (FIS) algorithm. Then, the trained algorithms are used to predict the sleep stages of real human patients. Extended comparison results are demonstrated which indicate that both classifiers could be utilized as a basis for an unobtrusive sleep quality assessment.
Copyright © 2015 J. Gialelis al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
