FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep State Classification.

OBJECTIVE

Fetal sleep state classification is essential for identifying neurodevelopmental complications like hypoxia, but manual annotation is subjective and labor intensive, and fetal EEG (fEEG) data is extremely scarce.

METHODS

We propose FetalSleepNet, the first deep learning architecture specifically developed for automated sleep staging from the fEEG. To address the scarcity of labeled data, we implement the first cross-developmental (adult-to fetal) and cross-species (human-to-sheep) transfer learning framework for this task, utilizing Spectral Equalisation (SE) to align the frequency-domain characteristics of adult human EEG with the fetal sheep target.

RESULTS

Our findings prove the irreplaceable effective ness of this adaptation: while direct transfer on raw EEG almost fails with only 18.7% accuracy, applying SE allows even a frozen model to reach 73.6% accuracy, effectively mitigating the cross-domain spectral mismatch. With full fine-tuning, FetalSleepNet achieves a state-of-the-art accuracy of 86.6% and a macro F1-score of 62.5%.

CONCLUSIONS

Beyond high-accuracy classification, Fetal SleepNet establishes a robust "label engine" paradigm. By generating high-fidelity neurophysiological annotations, it facilitates a framework for training proxy sleep stagers on broader, non-invasive clinical modalities. This paves the way for scalable, real-time fetal monitoring and early risk prediction in clinical settings.