AIBullisharXiv – CS AI · 9h ago6/10
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STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
Researchers propose STDA-Net, a deep learning framework for sleep stage classification that uses 2D spectrograms instead of traditional 1D EEG signals, combined with domain adaptation techniques to work across different datasets. The method achieves 89.03% accuracy and demonstrates superior stability compared to existing approaches, advancing automated sleep staging technology.