Polysomnography (PSG), the gold standard test for sleep analysis, generates vast amounts of multimodal clinical data, presenting an opportunity to leverage self-supervised representation learning (SSRL) for pre-training foundation models to enhance sleep analysis. However, progress in sleep foundation models is hindered by two key limitations: (1) the lack of a shared dataset and benchmark with diverse tasks for training and evaluation, and (2) the absence of a systematic evaluation of SSRL approaches across sleep-related tasks. To address these gaps, we introduce Stanford Sleep Bench, a large-scale PSG dataset comprising 17,467 recordings totaling over 163,000 hours from a major sleep clinic, including 13 clinical disease prediction tasks alongside canonical sleep-related tasks such as sleep staging, apnea diagnosis, and age estimation. We systematically evaluate SSRL pre-training methods on Stanford Sleep Bench, assessing downstream performance across four tasks: sleep staging, apnea diagnosis, age estimation, and disease and mortality prediction. Our results show that multiple pretraining methods achieve comparable performance for sleep staging, apnea diagnosis, and age estimation. However, for mortality and disease prediction, contrastive learning significantly outperforms other approaches while also converging faster during pretraining. To facilitate reproducibility and advance sleep research, we will release Stanford Sleep Bench along with pretrained model weights, training pipelines, and evaluation code.

翻译：多导睡眠图（PSG）作为睡眠分析的黄金标准测试，生成了海量的多模态临床数据，这为利用自监督表示学习（SSRL）预训练基础模型以提升睡眠分析提供了机遇。然而，睡眠基础模型的发展受到两个关键限制的阻碍：（1）缺乏用于训练和评估的共享数据集及包含多样化任务的基准测试；（2）缺乏对SSRL方法在睡眠相关任务上的系统性评估。为填补这些空白，我们引入了斯坦福睡眠基准，这是一个大规模PSG数据集，包含来自一家主要睡眠诊所的17,467条记录，总计超过163,000小时，涵盖13项临床疾病预测任务以及经典的睡眠相关任务，如睡眠分期、呼吸暂停诊断和年龄估计。我们在斯坦福睡眠基准上系统评估了SSRL预训练方法，通过四项下游任务评估其性能：睡眠分期、呼吸暂停诊断、年龄估计以及疾病和死亡率预测。我们的结果表明，多种预训练方法在睡眠分期、呼吸暂停诊断和年龄估计任务上取得了相当的性能。然而，对于死亡率和疾病预测，对比学习显著优于其他方法，同时在预训练期间收敛更快。为促进可重复性并推动睡眠研究，我们将发布斯坦福睡眠基准，包括预训练模型权重、训练流程和评估代码。