Temporal–Spectral–Spatial Multidimensional Feature Fusion-Based Heterogeneous Graph Adaptive Neural Network for Sleep Stage Classification

Sleep stage classification is essential for diagnosing sleep disorders and assessing sleep quality. However, achieving accurate classification remains a challenge because of the non-Euclidean spatial distribution of electroencephalography (EEG) electrodes. Existing methods often focus on a single dimension (e.g., the temporal, spectral, or spatial domain) or at most two dimensions of EEG signals, failing to fully capture their temporal–spectral–spatial multidimensional features and dynamic correlations. In addition, the heterogeneity of and complex interactions between brain regions, as well as individual variability, further hinder robust classification. To tackle this challenge, this article presents a new heterogeneous graph adaptive neural network (THSSleepNet), which employs temporal–spectral–spatial multidimensional feature fusion. The network represents EEG signals as a heterogeneous graph sequence structure, incorporating the characteristics of their temporal–spatial and spectral–spatial correlations. THSSleepNet comprises two branches, temporal–spatial streams and spectral–spatial streams, enabling comprehensive feature extraction across the temporal, spectral, and spatial dimensions. The model incorporates local and global temporal–spatial and spectral–spatial heterogeneity of brain regions using the dynamic multiscale path generation module (DMPGM). In addition, the graph attention module captures intricate interactions among brain regions, while the temporal/spectral adaptive module adaptively accounts for cross-scale dynamic context dependence across temporal–spectral dimensions. Subsequently, a hierarchical feature pyramid fusion (HFPF) module is employed to fuse temporal–spectral–spatial features of EEG signals. In addition, a domain adversarial learning mechanism mitigates the effect of individual variability on classification performance. The experimental results indicate that THSSleepNet surpasses current methods on the publicly available datasets, demonstrating its great potential for use in sensor-based EEG signal analysis and sleep monitoring.