Research ArticleOpen Access

EEG-Based Automatic Sleep Stage Classification

Volume 7 - Issue 4

Katerina D Tzimourta¹, Athanasios Tsilimbaris^2,3, Katerina Tzioukalia³, Alexandros T Tzallas³, Markos G Tsipouras³, Loukas G Astrakas¹ and Nikolaos Giannakeas³*

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- ¹Department of Medical Physics, School of Medicine, University of Ioannina, Greece
- ²School of Biological Sciences, The University of Edinburgh, Roger Land Building, United Kingdom
- ³Department of Computer Engineering, Technological Educational Institute of Epirus, Greece
*Corresponding author: Nikolaos Giannakeas, Department of Computer Engineering, School of Applied Technology, Technological Educational Institute of Epirus, Greece

Received: July 31, 2018; Published: August 06, 2018

DOI: 10.26717/BJSTR.2018.07.001535

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Abstract

Sleep disorders have a great impact in the patients’ quality of life. The study of human sleep during the different sleep stages is crucial in the diagnosis of sleep disorders and is mainly performed with polysomnography (PSG). In this work, a methodology for sleep staging using solely Electroencephalographic (EEG) signals from PSG recordings is presented. EEG signals from the ISRUC-Sleep dataset are selected and used, aiming to automatically identify the five sleep stages. Initially, the EEG signal is filtered in order to extract the five EEG rhythms and the energy is calculated in each sub-band and used to train several typical classifiers. Results in terms of classification accuracy reached 75.29% with Random Forests.

Keywords: Sleep Staging; Isruc Database; Automatic Sleep Stage Classification; Assc; EEG; Energy; Random Forests

Abbreviations: SAS: Sleep Apnea Syndrome; REM: Rapid Eye Movement; NREM: Non-Rapid Eye Movement; AASM: American Academy of Sleep Medicine; ASSC: Automatic Sleep Staging Classification; PSG: Polysomnography; EEG: Electroencephalogram; EOG: Electrooculogram; EMG: Electromyography; MODWT: Maximum Overlap Discrete Wavelet Transform; SVM: Support Vector Machines; KNN: k-Nearest Neighbor