# Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models

arXiv: https://arxiv.org/abs/2512.09591 · HTML: https://mlsanity.com/paper/2512.09591/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 61 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [One Fits All:Power General Time Series Analysis by Pretrained LM](https://mlsanity.com/paper/2302.11939/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Large-scale Training of Foundation Models for Wearable Biosignals](https://mlsanity.com/paper/2312.05409/) (2023)
- [XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging](https://mlsanity.com/paper/2007.05492/) (2020)
- [Scaling Wearable Foundation Models](https://mlsanity.com/paper/2410.13638/) (2024)
- [SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach](https://mlsanity.com/paper/1903.02108/) (2019)
- [Neural network an1alysis of sleep stages enables efficient diagnosis of narcolepsy](https://mlsanity.com/paper/1710.02094/) (2017)
- [SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signals](https://mlsanity.com/paper/2405.17766/) (2024)
- [A Large Collection of Real-world Pediatric Sleep Studies](https://mlsanity.com/paper/2102.13284/) (2021)
- [Automatic sleep stage classification with deep residual networks in a mixed-cohort setting](https://mlsanity.com/paper/2008.09416/) (2020)
- [MSED: a multi-modal sleep event detection model for clinical sleep analysis](https://mlsanity.com/paper/2101.02530/) (2021)
- [Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series](https://mlsanity.com/paper/2411.12824/) (2024)
- [Automated Respiratory Event Detection Using Deep Neural Networks](https://mlsanity.com/paper/2101.04635/) (2021)

## Built on by (most cited first, 3 of 3)

- [OSF: On Pre-training and Scaling of Sleep Foundation Models](https://mlsanity.com/paper/2603.00190/) (2026)
- [Pretraining on Sleep Data Improves non-Sleep Biosignal Tasks](https://mlsanity.com/paper/2605.02500/) (2026)
- [Learning transferable human physiology from two million hours of sleep with SleepFM-2](https://mlsanity.com/paper/2609.06849/) (2026)

## Abstract (arXiv)

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.