STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
Modulation-based feature extraction for robust sleep stage classification across apnea-based cohorts
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Demographic-stratified fine-tuning of a convolutional recurrent sleep staging model improves Cohen's kappa by 0.9-12.9% over a single population-agnostic baseline on 100 clinical PSG recordings.
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STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification
STDA-Net achieves 89.03% average accuracy and 87.64% macro F1 in cross-dataset sleep staging by processing 2D spectrograms with temporal modeling and unsupervised adversarial alignment, outperforming 1D baselines with lower variance.
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Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnography
Demographic-stratified fine-tuning of a convolutional recurrent sleep staging model improves Cohen's kappa by 0.9-12.9% over a single population-agnostic baseline on 100 clinical PSG recordings.