Extracting continuous sleep depth from EEG data without machine learning

TL;DR

This study demonstrates that sleep depth can be represented as a continuous variable derived from EEG data using PCA, challenging the traditional discrete sleep stage classification and enabling simpler sleep monitoring.

Contribution

The paper introduces a novel approach to extract a continuous sleep depth measure from EEG data without machine learning, using PCA to reveal underlying sleep dynamics.

Findings

Sleep stages do not cluster in raw EEG data.

A principal component, C_1(t), correlates strongly with sleep depth.

C_1(t) can serve as a continuous sleep variable.

Abstract

The human sleep-cycle has been divided into discrete sleep stages that can be recognized in electroencephalographic (EEG) and other bio-signals by trained specialists or machine learning systems. It is however unclear whether these human-defined stages can be re-discovered with unsupervised methods of data analysis, using only a minimal amount of generic pre-processing. Based on EEG data, recorded overnight from sleeping human subjects, we investigate the degree of clustering of the sleep stages using the General Discrimination Value as a quantitative measure of class separability. Virtually no clustering is found in the raw data, even after transforming the EEG signals of each thirty-second epoch from the time domain into the more informative frequency domain. However, a Principal Component Analysis (PCA) of these epoch-wise frequency spectra reveals that the sleep stages separate significantly better in the low-dimensional sub-space of certain PCA components. In particular the component $C_1(t)$ can serve as a robust, continuous 'master variable' that encodes the depth of sleep and therefore correlates strongly with the 'hypnogram', a common plot of the discrete sleep stages over time. Moreover, $C_1(t)$ shows persistent trends during extended time periods where the sleep stage is constant, suggesting that sleep may be better understood as a continuum. These intriguing properties of $C_1(t)$ are not only relevant for understanding brain dynamics during sleep, but might also be exploited in low-cost single-channel sleep tracking devices for private and clinical use.