Automatic sleep staging methods designed on healthy datasets often suffer from performance decline when applied to patients with obstructive sleep apnea (OSA). We attribute We attribute this to two factors: First, OSA sleep exhibits atypical inter-stages dependencies that are not captured by short-context models; Second, many prior approaches rely on one or a few channels and thus ignore the rich multichannel relationships present in clinical Polysomnography (PSG) recordings. To address these issues, we propose TransGATNet, which fuses long-term temporal-frequency features using a Graph-Attentional Transformer. Specifically, each TransGAT layer applies a Transformer encoder to capture global channel context, followed by a top-k sparsified graph attention network to isolate the most informative inter-electrode relationships. On the Sleep-EDF-2018 dataset, our TransGATNet achieves 86.8% in accuracy and 81.7% in macro-F1, and it retains 84.2% in accuracy and 81.0% in macro-F1 on our OSA dataset, demonstrating its generalizability on both healthy and OSA populations.