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. 2022 Apr 17;22(8):3079.
doi: 10.3390/s22083079.

Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages

Iqram Hussain  1 , Md Azam Hossain  2 , Rafsan Jany  2 , Md Abdul Bari  2 , Musfik Uddin  2 , Abu Raihan Mostafa Kamal  2 , Yunseo Ku  3 , Jik-Soo Kim  4
Affiliations

Quantitative Evaluation of EEG-Biomarkers for Prediction of Sleep Stages

Iqram Hussain et al. Sensors (Basel). .

Abstract

Electroencephalography (EEG) is immediate and sensitive to neurological changes resulting from sleep stages and is considered a computing tool for understanding the association between neurological outcomes and sleep stages. EEG is expected to be an efficient approach for sleep stage prediction outside a highly equipped clinical setting compared with multimodal physiological signal-based polysomnography. This study aims to quantify the neurological EEG-biomarkers and predict five-class sleep stages using sleep EEG data. We investigated the three-channel EEG sleep recordings of 154 individuals (mean age of 53.8 ± 15.4 years) from the Haaglanden Medisch Centrum (HMC, The Hague, The Netherlands) open-access public dataset of PhysioNet. The power of fast-wave alpha, beta, and gamma rhythms decreases; and the power of slow-wave delta and theta oscillations gradually increases as sleep becomes deeper. Delta wave power ratios (DAR, DTR, and DTABR) may be considered biomarkers for their characteristics of attenuation in NREM sleep and subsequent increase in REM sleep. The overall accuracy of the C5.0, Neural Network, and CHAID machine-learning models are 91%, 89%, and 84%, respectively, for multi-class classification of the sleep stages. The EEG-based sleep stage prediction approach is expected to be utilized in a wearable sleep monitoring system.

Keywords: electroencephalogram; machine-learning; neuroscience; physiological biomarker; polysomnography; sleep monitoring; sleep stages.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Methodology of EEG-based sleep stages classification using a machine-learning approach.
Figure 2
Figure 2
Results from EEG spectral power features during sleep stages W, N1, N2, N-3, and R. The bar chart describes the relative mean power of the EEG waves, and the vertical error bar (black color) is the 95% CI. (a) Alpha relative power for sleep stages in the frontal lobe, central lobe, occipital lobe, and global. (b) Beta relative power for sleep stages in the frontal lobe, central lobe, occipital lobe, and global. (c) Theta relative power for sleep stages in the frontal lobe, central lobe, occipital lobe, and global. (d) Delta relative power for sleep stages in the frontal lobe, central lobe, occipital lobe, and global. (e) Gamma relative power for sleep stages in the frontal lobe, central lobe, occipital lobe, and global. Global indicates the average measures of features of the frontal, central, and occipital lobes. The horizontal bars (brown color) are the outcomes of the hypothesis tests and indicate significant differences (p < 0.05) in EEG features among the sleep stages.
Figure 3
Figure 3
Results from DAR, DTR, and DTABR during sleep stages W, N1, N2, N-3, and R. The bar chart describes the relative mean power of the EEG waves and the vertical error bar (black color) is the 95% CI. Global indicates the average measures of features of the frontal, central, and occipital lobes. The horizontal bars (brown color) are the outcomes of the hypothesis tests and indicate significant differences (p < 0.05) in EEG features among the sleep stages.
Figure 4
Figure 4
Performance of the three machine-learning models (C5.0, Neural Network, and CHAID Models) to classify the sleep stages W, N1, N2, N-3, and R using training and testing datasets of EEG features.
See this image and copyright information in PMC

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