Efficient sleep stage classification based on EEG signals

• Published in: LISAT 2014 : IEEE Long Island Systems, Applications and Technology Conference : 2 May 2014 pp. 1 - 6
• Main Authors: Aboalayon, Khald A. I., Ocbagabir, Helen T., Faezipour, Miad
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2014
• Subjects: Band-pass filters; classification; EEG; EEG sub-bands; Electroencephalography; Entropy; Feature extraction; Sleep; sleep stages; Support vector machines; SVM
• DOI: 10.1109/LISAT.2014.6845193

Currently, sleep disorders are considered as one of the major human life issues. There are several stable physiological stages that the human brain goes through during sleep. Nowadays, many biomedical signals such as EEG, ECG, EMG, and EOG offer useful details for clinical setups that are used in identifying sleep disorders. In this work, we propose an efficient technique that could be implemented in hardware to differentiate sleep stages which will assist physicians in the diagnosis and treatment of related sleep disorders. This study depends on different EEG datasets from PhysioNet using the Sleep-EDF [Expanded] Database that were acquired and described by scientists for the analysis and diagnosis of sleep stages. Generally, the EEG signal is decomposed into five bands: delta, theta, alpha, beta, and gamma to define the change in brain state. In this work, Butterworth band-pass filters are designed to filter and decompose EEG into the above frequency sub-bands. In addition, various discriminating features including energy, standard deviation and entropy are computed and extracted from each δ, □, α, β and γ sub-band. Furthermore, the extracted features are then fed to a supervised learning classifier; support vector machine (SVM) to be able to recognize the sleep stages state and identify if the acquired signal is corresponding to wake or stage 1 of sleep, according to the purpose of this research. The key novelty of this work is to identify the sleep stages from a publicly available EEG signal dataset by using a feasible set of features, easily implementable filters in any microcontroller device, and an efficient classification method. Therefore, physicians can track these sleep stages to identify certain patterns such as detecting fatigue, drowsiness, and/or various sleep disorders such as sleep apnea. The experimental results on a variety of subjects verify 92.5% of classification accuracy of the proposed work.