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

TL;DR

ST-USleepNet is a novel deep learning framework that captures spatial-temporal coupling patterns from multi-channel sleep signals, improving sleep staging accuracy by effectively modeling complex sleep features.

Contribution

It introduces a spatial-temporal graph construction module and a U-shaped network architecture to better extract sleep features from raw multi-channel signals.

Findings

Outperforms existing sleep staging methods on multiple datasets.

Effectively captures prominent temporal and spatial sleep features.

Visualizations confirm accurate modeling of spatial-temporal coupling patterns.

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.