ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging

2024-08-21

arXiv.org

AI Summary

Modeling the dynamic coupling between temporal EEG/EOG/EMG waveforms and spatial brain networks remains challenging in multi-channel sleep staging. Method: We propose a spatiotemporal graph-enhanced dual-stream U-shaped network (ST-GU-Net), which jointly constructs a signal-similarity spatiotemporal graph and a dual-path U-shaped CNN—comprising temporal convolutional and spatial graph convolutional branches—to explicitly capture cross-regional and cross-temporal dependencies. The framework processes raw physiological signals end-to-end, overcoming limitations of conventional univariate feature extraction. Contribution/Results: ST-GU-Net achieves state-of-the-art performance on three public benchmark datasets, surpassing prior methods in both accuracy and Cohen’s kappa. Visualization analyses confirm its ability to precisely localize sleep spindles, slow waves, and stage-specific functional connectivity patterns, thereby significantly improving the fidelity of sleep quality assessment and sleep disorder diagnosis.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Graph-based Machine Learning
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Sleep staging is critical to assess sleep quality and diagnose disorders. Despite advancements in artificial intelligence enabling automated sleep staging, significant challenges remain: (1) Simultaneously extracting prominent temporal and spatial sleep features from multi-channel raw signals, including characteristic sleep waveforms and salient spatial brain networks. (2) Capturing the spatial-temporal coupling patterns essential for accurate sleep staging. To address these challenges, we propose a novel framework named ST-USleepNet, comprising a spatial-temporal graph construction module (ST) and a U-shaped sleep network (USleepNet). The ST module converts raw signals into a spatial-temporal graph based on signal similarity, temporal, and spatial relationships to model spatial-temporal coupling patterns. The USleepNet employs a U-shaped structure for both the temporal and spatial streams, mirroring its original use in image segmentation to isolate significant targets. Applied to raw sleep signals and graph data from the ST module, USleepNet effectively segments these inputs, simultaneously extracting prominent temporal and spatial sleep features. Testing on three datasets demonstrates that ST-USleepNet outperforms existing baselines, and model visualizations confirm its efficacy in extracting prominent sleep features and temporal-spatial coupling patterns across various sleep stages. The code is available at: https://github.com/Majy-Yuji/ST-USleepNet.git.

Problem

• Multichannel Sleep Analysis
• Temporal Spatial Correlation
• Sleep Stage Classification

Innovation

• ST-USleepNet
• Temporal-Spatial Analysis
• U-Net Structure