标题： 用更少获得更多的：迁移学习提高了外围可穿戴设备的睡眠阶段解码准确性 显示英文标题

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

摘要： 迁移学习，一种在生成式人工智能中常用的技术，允许神经网络模型在学习新任务时运用先前的知识。 本研究证明了迁移学习能够显著提高从外围可穿戴设备解码睡眠阶段的准确性，方法是利用在脑电图（EEG）信号上预训练的神经网络模型。 消费级可穿戴技术通常依赖于外围生理信号，例如脉搏血氧仪（PPG）和呼吸数据，虽然这些信号方便获取，但它们缺乏临床EEG在详细睡眠分期分类中的精确性。 我们在一个大型公开可用的EEG数据集上预训练了一个基于Transformer的神经网络，并随后在更嘈杂的外围信号上微调了该模型。 我们的迁移学习方法将整体分类准确率从67.6%（基准模型仅在外围信号上训练）提升到了76.6%。 在各个睡眠阶段都观察到了显著的准确率提升，特别是在较轻的睡眠阶段，如快速眼动（REM）和N1期。 这些结果表明，迁移学习有可能显著提高消费级可穿戴设备的准确性和实用性，而无需改变现有的硬件配置。 未来整合自监督学习方法可能会进一步提升性能，促进更精确、长期的睡眠监测，以支持个性化健康应用。 显示英文摘要

摘要： 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.