EPS
← All batches·2605.06736

STDA-Net: Spectrogram-Based Domain Adaptation for cross-dataset Sleep Stage Classification

topic: othertop score: 50released: 2026-05-11first surfaced: 2026-05-11arXivPDFnew_research2026-05-11

Authors: Unaza Tallal, Shruti Kshirsagar, Ankita Shukla

arXiv · PDF

Summary

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

Relevance

No connection to my persona-installation or language-model work—this is a domain-specific medical signal-processing paper on sleep staging. Included for completeness but entirely outside my research area.

Abstract

arXiv:2605.06736v1 Announce Type: new Abstract: Accurate sleep stage classification across datasets remains challenging due to variability in EEG channel montages, sampling rates, recording environments, and subject populations. Although deep learning has shown considerable promise for automated sleep staging, most existing cross-dataset methods rely on one-dimensional EEG signal representations, whereas the use of two-dimensional spectrogram-based inputs within an unsupervised domain adaptation framework has remained largely unexplored. Here, we propose STDA-Net (Spectrogram-based Temporal Domain Adaptation Network), a framework that combines a convolutional neural network (CNN) for spectrogram-based feature extraction, a bidirectional long short-term memory (BiLSTM) module for temporal modeling of sleep dynamics, and a domain-adversarial neural network (DANN) for source-to-target feature alignment without requiring any labeled target-domain data during training. Experiments are conducted on three publicly available datasets Sleep-EDF, SHHS-1, and SHHS-2 under six cross-dataset transfer settings. Results show that the proposed framework achieves an average accuracy of 89.03% and an average macro F1-score of 87.64%, consistently outperforming existing 1D baseline methods in terms of balanced classification performance, with substantially lower variance across five independent runs, indicating improved stability and reproducibility. Overall, these findings demonstrate that 2D spectrogram-based representations, combined with temporal modeling and adversarial domain adaptation, provide a robust and competitive alternative to conventional 1D EEG inputs for cross-dataset sleep staging.