Abstract
Graph-based sleep-staging models use different node representations, making it difficult to separate the contribution of the frontend from that of the graph architecture. We investigate whether a common raw-waveform encoder can improve heterogeneous graph models without model- or dataset-specific architectural tuning. We replace the Native frontends of five graph backbones with the same compact one-dimensional convolutional neural network (CNN). The CNN learns node representations directly from 30 s polysomnographic waveforms, while each model retains its original graph-construction mechanism, temporal context, and prediction modules. Three training strategies are compared across five datasets using matched five-fold subject-wise cross-validation: two-stage training (CNN–2S), end-to-end training (CNN–E2E), and pretraining followed by fine-tuning (CNN–PTFT). At least one CNN strategy outperforms the corresponding Native frontend in 22 of the 25 backbone–dataset combinations. Mean macro-F1 gains are 2.99, 2.34, and 2.53 percentage points for CNN–2S, CNN–E2E, and CNN–PTFT, respectively. Gains are positive on average for all sleep stages and are largest for N1 and REM. CNN–2S gives the strongest overall results and the most favorable aggregate performance–cost balance, although the effect on training time varies across backbones. The frozen CNN–2S representation also remains effective when transferred across datasets. The findings support node-representation learning as an important component of graph-based sleep-staging design.