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. 2023 Jun 29:15:479-490.
doi: 10.2147/NSS.S401270. eCollection 2023.

Spotlight on Sleep Stage Classification Based on EEG

Affiliations

Spotlight on Sleep Stage Classification Based on EEG

Isabelle Lambert et al. Nat Sci Sleep. .

Abstract

The recommendations for identifying sleep stages based on the interpretation of electrophysiological signals (electroencephalography [EEG], electro-oculography [EOG], and electromyography [EMG]), derived from the Rechtschaffen and Kales manual, were published in 2007 at the initiative of the American Academy of Sleep Medicine, and regularly updated over years. They offer an important tool to assess objective markers in different types of sleep/wake subjective complaints. With the aims and advantages of simplicity, reproducibility and standardization of practices in research and, most of all, in sleep medicine, they have overall changed little in the way they describe sleep. However, our knowledge on sleep/wake physiology and sleep disorders has evolved since then. High-density electroencephalography and intracranial electroencephalography studies have highlighted local regulation of sleep mechanisms, with spatio-temporal heterogeneity in vigilance states. Progress in the understanding of sleep disorders has allowed the identification of electrophysiological biomarkers better correlated with clinical symptoms and outcomes than standard sleep parameters. Finally, the huge development of sleep medicine, with a demand for explorations far exceeding the supply, has led to the development of alternative studies, which can be carried out at home, based on a smaller number of electrophysiological signals and on their automatic analysis. In this perspective article, we aim to examine how our description of sleep has been constructed, has evolved, and may still be reshaped in the light of advances in knowledge of sleep physiology and the development of technical recording and analysis tools. After presenting the strengths and limitations of the classification of sleep stages, we propose to challenge the "EEG-EOG-EMG" paradigm by discussing the physiological signals required for sleep stages identification, provide an overview of new tools and automatic analysis methods and propose avenues for the development of new approaches to describe and understand sleep/wake states.

Keywords: artificial intelligence; automatic; electrophysiology; recommendation; scoring; visual.

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Conflict of interest statement

The authors report no financial or non-financial conflicts of interest in this work.

Figures

Figure 1
Figure 1
Intra-Epoch Heterogeneity In Sleep Stages. In this N2 epoch, one can see both N2 features (in blue: low muscle tone, K complex, sleep spindles) and R features (in green: sawtooth waves and transient atonia). This example highlights that the 30s scale does not capture the complexity of the dynamics of vigilance states.
Figure 2
Figure 2
Peak-to-peak amplitude of slow waves according to different EEG montages. This N3 epoch is shown with the AASM recommended referential montage (A) and in a bipolar montage (B) with transverse (up) and longitudinal (down) channels. Both panels are visualized with an amplitude of 10µV/mm for EEG channels. The peak-to-peak amplitude of the same slow wave is measured on two different channels (black arrow). The amplitude is higher using the AASM recommended montage on the frontal channel (220.92 µV in (A) compared to the transverse montage (−103.85 µV in (B) with a negative value due to the inverse polarity). Also note that in (B) the maximal amplitude of this slow wave is not in observed frontal channels.
Figure 3
Figure 3
EEG Time-Frequency Analysis During Sleep Recording. Hypnogram obtained from AASM recommended sleep staging in (A and B) shows the corresponding spectrogram trend of EEG frequency measured on C4-M1 channel derived from a fast Fourier transform analysis. Color-bar indicates the power of EEG frequency (µV/Hz with the lowest power in dark blue and the highest power in red), the y-axis indicates the EEG frequency (from 0 to 30 Hz) and the x-axis indicates time. The EEG signatures of NREM include an increase in the delta band below 2 Hz (Orange arrows, compatible with slow waves) and in the sigma band around 15 Hz (red arrows, compatible with spindle activity). The power of delta activity decreases across the night along with the decrease of homeostatic pressure. An increase in beta activity is observed in REM sleep (green arrows). At the end of the recording, note the EEG activity in the alpha band around 10 Hz (blue arrows, compatible with quiet wakefulness eyes closed).

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