Accurate sleep staging for patients with sleep disorders requires the integration of multiple channels and various signal modalities. While Graph Convolutional Networks (GCNs) have significantly improved sleep staging accuracy by effectively processing complex data, current GCN-based methods often require a large number of signal channels and struggle to capture temporal correlations effectively. They are leading to a large limitation in the scope of graph network models in sleep staging. To address these challenges, we propose an attention based Spatial-Temporal Graph Network for sleep staging, named ASTGSleep. ASTGSleep effectively addresses the temporal continuity of sleep data and overcomes limitations related to the number of channels by integrating the Temporal Graph Construction and Feature Extraction Module (TGFEM). TGFEM captures dynamic temporal features through temporal graph construction, complementing spatial features and enhancing sensitivity to sleep stage transitions. Moreover, the Multi-Dimensional Fusion Module (MDFM) optimizes and fuses these spatio-temporal features using a graph attention network, further improving accuracy and robustness in sleep staging. Experimental results demonstrate that ASTGSleep achieves competitive accuracy on datasets with different numbers of channels, such as MASSSS3 with 26 channels (87.0%) and SleepEDF-20 with 3 channels (86.5%). Furthermore, its performance on sleep disorder datasets, including CAP (81.4%) and clinical CZ-RBD (82.7%), shows robust generalization and enhanced accuracy, making it a promising tool for automatic sleep staging in clinical diagnostics.

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are either suffering from dramatic performance drops when coping with varying input modalities or unable to handle heterogeneous signals. To handle heterogeneous signals and guarantee favorable sleep staging performance when a single modality is available, a pseudo-siamese neural network (PSN) to incorporate electroencephalography (EEG), electrooculography (EOG) characteristics is proposed (PSEENet). PSEENet consists of two parts, spatial mapping modules (SMMs) and a weight-shared classifier. SMMs are used to extract high-dimensional features. Meanwhile, joint linkages among multi-modalities are provided by quantifying the similarity of features. Finally, with the cooperation of heterogeneous characteristics, associations within various sleep stages can be established by the classifier. The evaluation of the model is validated on two public datasets, namely, Montreal Archive of Sleep Studies (MASS) and SleepEDFX, and one clinical dataset from Huashan Hospital of Fudan University (HSFU). Experimental results show that the model can handle heterogeneous signals, provide superior results under multimodal signals and show good performance with single modality. PSEENet obtains accuracy of 79.1%, 82.1% with EEG, EEG and EOG on Sleep-EDFX, and significantly improves the accuracy with EOG from 73.7% to 76% by introducing similarity information.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional raw polysomnograms or two-dimensional spectrograms, which omit critical details due to single-view processing. This shortcoming is particularly apparent in pediatric sleep staging, where the lack of a specialized network fails to meet the needs of precision medicine. Therefore, we introduce AFSleepNet, a novel attention-based multi-view feature fusion network tailored for pediatric sleep analysis. The model utilizes multimodal data (EEG, EOG, EMG), combining one-dimensional convolutional neural networks to extract time-invariant features and bidirectional-long-short-term memory to learn the transition rules among sleep stages, as well as employing short-time Fourier transform to generate two-dimensional spectral maps. This network employs a fusion method with self-attention mechanism and innovative pre-training strategy. This strategy can maintain the feature extraction capabilities of AFSleepNet from different views, enhancing the robustness of the multi-view model while effectively preventing model overfitting, thereby achieving efficient and accurate automatic sleep stage analysis. A "leave-one-subject-out" cross-validation on CHAT and clinical datasets demonstrated the excellent performance of AFSleepNet, with mean accuracies of 87.5% and 88.1%, respectively. Superiority over existing methods improves the accuracy and reliability of pediatric sleep staging.

Measuring causal brain network is a significant topic for exploring complex brain functions. While various data-driven algorithms have been proposed, they still have some drawbacks such as ignoring time non-separability, cumbersome parameter settings, and poor robustness. To solve these deficiencies, we developed a novel framework: “time-shift permutation cross-mapping, TPCM,” integrating steps of (1) delayed improved phase-space reconstruction (DIPSR), (2) rank transformation of embedding vectors’ distances, (3) cross-mapping with a fitting estimation, and (4) causality quantification using multi-delays. Based on synthetic models and comparison with baseline methods, numerical validation results demonstrate that TPCM significantly improves the robustness for data length with or without noise interference, and achieves the best quantification accuracy in detecting time delay and coupling strength, with the highest determination coefficient ( R ² = 0. 96 ) of fitting verse coupling parameters. The developed TPCM was finally applied to ictal electrocorticogram (ECoG) analysis of patients with drug-resistant epilepsy (DRE). A total of 17 patients with DRE were included into the retrospective study. For 8 patients undergoing successful surgeries, the causal coupling strength (0.58 ± 0.20) within epileptogenic zone network is significantly higher than those suffering failed surgeries (0.38 ± 0.16) with P < 0. 001 through Mann-Whitney-U-test. Therefore, the epileptic brain network measured by TPCM is a credible biomarker for predicting surgical outcomes. These findings additionally confirm TPCM’s superior performance and promising potential to advance precision medicine for neurological disorders.

Obstructive sleep apnea (OSA), one of the most common sleep disorders globally, is closely linked to brain function. Resting-state electroencephalography (EEG), due to its convenience, cost-effectiveness, and high temporal resolution, serves as a valuable tool for exploring the human brain function. This study utilized a large cohort with 968 participants who joined in 15-minute daytime resting-state EEG acquisition and overnight polysomnography (PSG) monitoring. Participants were categorized into healthy controls and mild, moderate, and severe OSA groups based on apnea-hypopnea index (AHI) derived from PSG data. Resting-state EEG functional connectivity (FC) was estimated using correlation (Corr), coherence (Coh), phase-locking value (PLV), and phase lag index (PLI). Results showed that FC between most nodes increased with the OSA severity, which suggest the potential neural compensation. However, regional decreases emerged in the right central, right frontal, left central, and left parieto-occipital regions. Higher frequency bands exhibited fewer enhanced FC connections. Graph-theoretical analysis revealed reduced centrality, indicating weakened communication hubs and potential topological reorganization. Multivariate analysis with adjustment of age, sex, and BMI, was also used as a feature selection strategy, identified effective FC features of OSA severity (p value < adjusted significance threshold, 2.15e-5). These FC features were used in machine learning models for severity classification and enhanced interpretability. The Corr-based XGBoost model achieved the highest performance, with an accuracy of 0.79 and AUC of 0.90. These findings highlight OSA-related brain function alterations and demonstrate that resting-state EEG FC provides a non-invasive, task-free and interpretable tool for OSA severity classification without disrupting natural sleep.