Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices
Journal:
arXiv
Published Date:
May 31, 2025
Abstract
Transfer learning, a technique commonly used in generative artificial
intelligence, allows neural network models to bring prior knowledge to bear
when learning a new task. This study demonstrates that transfer learning
significantly enhances the accuracy of sleep-stage decoding from peripheral
wearable devices by leveraging neural network models pretrained on
electroencephalographic (EEG) signals. Consumer wearable technologies typically
rely on peripheral physiological signals such as pulse plethysmography (PPG)
and respiratory data, which, while convenient, lack the fidelity of clinical
electroencephalography (EEG) for detailed sleep-stage classification. We
pretrained a transformer-based neural network on a large, publicly available
EEG dataset and subsequently fine-tuned this model on noisier peripheral
signals. Our transfer learning approach improved overall classification
accuracy from 67.6\% (baseline model trained solely on peripheral signals) to
76.6\%. Notable accuracy improvements were observed across sleep stages,
particularly lighter sleep stages such as REM and N1. These results highlight
transfer learning's potential to substantially enhance the accuracy and utility
of consumer wearable devices without altering existing hardware. Future
integration of self-supervised learning methods may further boost performance,
facilitating more precise, longitudinal sleep monitoring for personalized
health applications.
