The authors propose STDA-Net, a framework for sleep-stage classification that works across different EEG datasets (which vary in channels, sampling rates, and recording environments). They combine a CNN on 2D spectrogram inputs, a BiLSTM to model sleep dynamics over time, and domain-adversarial training (DANN) to align source and target datasets without needing labeled target data. Tested on Sleep-EDF, SHHS-1, and SHHS-2, the approach achieves ~89% accuracy and ~88% macro F1 with lower variance than 1D baseline methods.
Main takeaways:
- Uses 2D spectrograms instead of 1D EEG signals as input to a CNN-BiLSTM architecture
- Applies domain-adversarial training to align features across datasets without labeled target data
- Achieves 89.03% average accuracy and 87.64% macro F1 across six cross-dataset transfer settings
- Shows substantially lower variance across runs than 1D baselines, indicating better stability