A Quantitative Framework for Assessing Sleep Quality from EEG Time Series in Complex Dynamic Systems

TL;DR

This study introduces a quantitative EEG-based framework using phase-amplitude coupling to assess sleep quality, revealing neural markers like delta-beta PAC that correlate with sleep health and enable individual classification.

Contribution

It presents a novel EEG analysis method focusing on delta-beta PAC as a biomarker for sleep quality, combining neurophysiological insights with machine learning classification.

Findings

Delta-beta PAC is stronger in individuals with good sleep quality.

Sleep quality correlates positively with delta-beta PAC levels.

Machine learning models using delta-beta PAC effectively classify sleep quality.

Abstract

Modern lifestyles contribute to insufficient sleep, impairing cognitive function and weakening the immune system. Sleep quality (SQ) is vital for physiological and mental health, making its understanding and accurate assessment critical. However, its multifaceted nature, shaped by neurological and environmental factors, makes precise quantification challenging. Here, we address this challenge by utilizing electroencephalography (EEG) for phase-amplitude coupling (PAC) analysis to elucidate the neurological basis of SQ, examining both states of sleep and wakefulness, including resting state (RS) and working memory. Our results revealed distinct patterns in beta power and delta connectivity in sleep and RS, together with the reaction time of working memory. A notable finding was the pronounced delta-beta PAC, a feature markedly stronger in individuals with good SQ. We further observed that SQ was positively correlated with increased delta-beta PAC. Leveraging these insights, we applied machine learning models to classify SQ at an individual level, demonstrating that the delta-beta PAC outperformed other EEG characteristics. These findings establish delta-beta PAC as a robust electrophysiological marker to quantify SQ and elucidate its neurological determinants.