bebi 20: e5

Research Article

Towards exploration and evaluation of sleep staging classification schemes for healthy and patient subjects

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  • @ARTICLE{10.4108/eai.19-10-2020.166665,
        author={Christos Timplalexis and Dimitrios Chasanidis and Ioanna Chouvarda and Konstantinos Diamantaras},
        title={Towards exploration and evaluation of sleep staging classification schemes for healthy and patient subjects},
        journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics},
        volume={},
        number={},
        publisher={EAI},
        journal_a={BEBI},
        year={2020},
        month={10},
        keywords={sleep staging, sleep classification, EEG, PSG, AASM},
        doi={10.4108/eai.19-10-2020.166665}
    }
    
  • Christos Timplalexis
    Dimitrios Chasanidis
    Ioanna Chouvarda
    Konstantinos Diamantaras
    Year: 2020
    Towards exploration and evaluation of sleep staging classification schemes for healthy and patient subjects
    BEBI
    EAI
    DOI: 10.4108/eai.19-10-2020.166665
Christos Timplalexis1,*, Dimitrios Chasanidis2, Ioanna Chouvarda3, Konstantinos Diamantaras2
  • 1: Information Technologies Institute/ Centre for Research and Technology Hellas, 6th Km Charilaou-Thermis 57001, Thermi-Thessaloniki, Greece
  • 2: Department of Information and Electronic Engineering, International Hellenic University, Sindos, 57400, Greece
  • 3: Lab of Computing, Medical Informatics and Biomedical Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece
*Contact email: ctimplalexis@iti.gr

Abstract

INTRODUCTION: Sleep stage classification is an important task for the timely diagnosis of sleep-related disorders, which are one the most common indicator of illness.

OBJECTIVE: An automated sleep scoring implementation with promising generalization capabilities is presented, aiding towards eliminating the tedious procedure of manual sleep scoring.

METHODS:Two Electroencephalogram (EEG) channels and the Electrooculogram (EOG) channel are utilized as inputs for feature extraction both in the time and frequency domain, while temporal feature changes are utilized in order to capture contextual information of the signals. An ensemble tree-based and a neural network approach are presented at the classification process.

RESULTS: A total of 66 subjects belonging to three different groups (healthy, placebo, drug intake) were included in the study. The tree-based classification method outperforms the neural network at all cases.

CONCLUSION: State of the art results are achieved, while it is highlighted that using jointly the healthy and patient subjects dataset, boosts the model’s accuracy and generalization capability.