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

2025-11-19

AI Summary

Chronic sleep deprivation, driven by modern lifestyles, severely impairs cognitive and immune functions; however, the multidimensional nature of sleep quality (SQ) hinders its precise, objective quantification. Method: We propose an EEG-based temporal modeling framework for SQ assessment, introducing delta-beta phase-amplitude coupling (PAC) as a novel neurobiomarker—demonstrating significant positive correlation with SQ and superior predictive power over conventional EEG features. Our approach integrates PAC estimation, functional connectivity modeling, and machine learning–based classification to construct an individualized SQ classifier. Results: The model achieves high accuracy during both sleep and wakeful resting-state conditions. Critically, we identify delta-beta PAC as a cross-state neural mechanism robustly encoding SQ—establishing the first evidence of its state-invariant relevance. This work introduces a label-free, objective, and interpretable paradigm for SQ evaluation grounded in mechanistic neurophysiology.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Humans and AI: Brain-Sensing and Analysis
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

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.

Problem

• Quantifying sleep quality using EEG phase-amplitude coupling analysis
• Identifying neurological markers through delta-beta coupling patterns
• Developing machine learning models for individual sleep quality classification

Innovation

• EEG phase-amplitude coupling analysis
• Delta-beta PAC as sleep quality marker
• Machine learning models for classification