phat 15(4): e5

Research Article

A Comparative Study between SVM and Fuzzy Inference System for the Automatic Prediction of Sleep Stages and the Assessment of Sleep Quality

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  • @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
John Gialelis1,*, Chris Panagiotou1, Ioakeim Samaras1, Petros Chondros1, Dimitris Karadimas1
  • 1: University of Patras
*Contact email: gialelis@ece.upatras.gr

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.