Getting More from Less: Transfer Learning Improves Sleep Stage Decoding Accuracy in Peripheral Wearable Devices

TL;DR

This paper demonstrates that transfer learning significantly improves sleep stage classification accuracy in consumer wearable devices by leveraging pretrained neural networks on EEG data, enabling better performance with peripheral signals.

Contribution

The study introduces a transfer learning approach using pretrained transformer models on EEG data to enhance sleep-stage decoding from peripheral signals in wearable devices.

Findings

Accuracy improved from 67.6% to 76.6%.

Significant gains in REM and N1 sleep stages.

Transfer learning enables better sleep monitoring without hardware changes.

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.