Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Models
Stanford University（斯坦福大学）；Technical University of Denmark（技术大学）；Danish Center for Sleep Medicine（丹麦睡眠医学中心）；University of Copenhagen（哥本哈根大学）；Harvard Medical School（哈佛医学院）
Magnus Ruud Kjaer, Rahul Thapa, Gauri Ganjoo, Hyatt Moore, Poul Joergen Jennum, Brandon M. Westover, James Zou, Emmanuel Mignot, Bryan He, Andreas Brink-Kjaer
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.
SemPSG: A Semantic Channel-Aware Foundation Model for Polysomnography Analysis
Center for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS（中国科学院香港创新研究院人工智能与机器人中心）；Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China（电子科技大学深圳高等研究院）；Technical University of Munich（慕尼黑工业大学）
Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS-ANS Dynamics
Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Omni-Intelligence（全知智能）；Shenzhen Loop Area Institute（深圳河套学院）
SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
Graduate School of Data Science, Seoul National University, Seoul, South Korea（首尔国立大学数据科学研究生院，韩国首尔）；Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Republic of Korea（首尔国立大学医学院生物医学科学系，韩国首尔）；Obstructive Upper Airway Research (OUaR) Laboratory, Department of Pharmacology, Seoul National University College of Medicine, Seoul, Republic of Korea（首尔国立大学医学院药理学系阻塞性上气道研究（OUaR）实验室，韩国首尔）；Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul, Republic of Korea（首尔国立大学医院耳鼻喉头颈外科系，韩国首尔）
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning
University of Essex（埃塞克斯大学）；University of Chemistry and Technology in Prague（布拉格化学与技术大学）；Charles University（查理大学）；Czech Technical University in Prague（布拉格捷克理工大学）；Nottingham Trent University（诺丁汉特伦特大学）
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
Korea University（高丽大学）；University of Cambridge（剑桥大学）；Stanford University School of Medicine（斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托退伍军人事务医疗系统）；Korea University College of Medicine（高丽大学医学院）
PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learning
School of Future Technology, South China University of Technology（华南理工大学未来技术学院）；Guangzhou First People’s Hospital（广州市第一人民医院）；School of Medicine, South China University of Technology（华南理工大学医学院）
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
Department of Brain and Cognitive Engineering, Korea University（脑科学与认知工程系，韩国大学）；Interdisciplinary Program in Precision Public Health, Korea University（精准公共卫生跨学科项目，韩国大学）；Department of Radiology, Stanford University School of Medicine（放射科，斯坦福大学医学院）；VA Palo Alto Health Care System（帕洛阿尔托医疗系统）；Department of Neurology, Korea University College of Medicine（神经病学系，韩国大学医学院）
Using Legacy Polysomnography Data to Train a Radar System to Quantify Sleep in Older Adults and People living with Dementia
Department of Electrical and Electronic Engineering and UK Dementia Research Institute (Care Research & Technology Centre), Imperial College London（电气与电子工程系和英国痴呆症研究机构（护理与技术研究中心）、帝国理工学院伦敦分校）；Department of Informatics, University of Oslo（信息系，奥斯陆大学）；School of Biosciences, University of Surrey（生物科学学院，萨里大学）
Deep learning-based multi-level airway collapse site localization on obstructive sleep apnea patients based on snoring sounds
National Taiwan University（台湾大学）；Taipei Tzu Chi Hospital（台北慈济医院）；Tzu Chi University（慈济大学）；Nova Southeastern University（诺瓦东南大学）；All Vista Healthcare Center（全视界医疗中心）；Chang Gung University（长庚大学）；Heroic Faith Medical Science Co., Ltd.（鸿信医学科技股份有限公司）；Chung Shan Medical University（中山医学大学）；Chung Shan Medical University Hospital（中山医学大学附设医院）
NAP: Attention-Based Late Fusion for Automatic Sleep Staging
Faculty of Informatics, Università della Svizzera Italiana（信息学院，瑞士意大利大学）；Inst. of Digital Tech. for Personalized Healthcare, SUPSI（个性化医疗数字技术研究所，SUPSI）；Sleep Wake Epilepsy Center, Department of Neurology, Inselspital University of Bern（睡眠觉醒癫痫中心，神经科，伯恩大学医院）；Faculty of Informatics Università della Svizzera Italiana（信息学院，瑞士意大利大学）
A Clinical Evaluation of a Low-Cost Strain Gauge Respiration Belt and Machine Learning to Detect Sleep Apnea
University of Oslo（奥斯陆大学）；Oslo University Hospital（奥斯陆大学医院）；Lovisenberg Diakonale Hospital（洛维森贝格迪亚科纳莱医院）；St. Olavs University Hospital（圣奥拉夫斯大学医院）；Norwegian University of Science and Technology（挪威科技大学）
Zhejiang University（浙江大学）；Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine（浙江大学医学院附属精神卫生中心&杭州市第七人民医院）；The First Affiliated Hospital, College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）
The Breakthrough of Sleep: A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp
Hong Kong Xsmart Century Technology Co., Ltd.（香港Xsmart世纪科技有限公司）；Urban Mobility Lab, Massachusetts Institute of Technology（城市移动实验室，麻省理工学院）；Singapore-MIT Alliance for Research and Technology (SMART) Centre（新加坡-麻省理工联盟研究技术中心）；Department of Respiratory and Critical Care Medicine, Binzhou Medical University Hospital（呼吸与危重症医学科，滨州医学院医院）；Interdisciplinary Center of Sleep Medicine, Charité – Universitätsmedizin Berlin（睡眠医学跨学科中心，夏里特-柏林大学医学中心）
A Recall-First CNN for Sleep Apnea Screening from Snoring Audio
Sai International School（国际学校）；Gems New Millennium School（新 Millennium 学校）；Queen's Valley School（Queen's Valley 学校）；Department of Computer Science and Engineering（计算机科学与工程系）；S.R.M Institute of Science and Technology（S.R.M 科学与技术学院）
On Sleep-Wake Disorder Identification from Polysomnographic Representations
Faculty of Informatics, Università della Svizzera Italiana（瑞士意大利语区大学信息学院）；MeDiTech, SUPSI（瑞士应用科学与艺术大学医学技术学院）；Ente Ospedaliero Cantonale (EOC)（州立医院管理局）；University of Brescia（布雷西亚大学）；University of Bern（伯尔尼大学）
Massachusetts Institute of Technology（麻省理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Emerald Innovations Inc.（Emerald Innovations公司）
ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregation
Sharif University of Technology（谢里夫理工大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Georgia Institute of Technology（佐治亚理工学院）；Emory School of Medicine（埃默里大学医学院）
Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging
Dreem（Dreem公司）；Stanford University（斯坦福大学）；French Armed Forces Biomedical Research Institute (IRBA)（法国武装部队生物医学研究所（IRBA））；Paris Descartes University（巴黎笛卡尔大学）
sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignals
Five Seasons Medical（五 Seasons 医疗）；Tsinghua University（清华大学）；Beijing Key Laboratory for Sleep Breathing Disorder（北京睡眠呼吸障碍重点实验室）；Peking University（北京大学）；Fudan University（复旦大学）；Technical University of Munich（慕尼黑技术大学）
Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health
National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Shanghai Jiao Tong University（上海交通大学）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；HeartVoice Medical Technology（心音医疗科技）；University of California, San Francisco（加州大学旧金山分校）
Pediatric Automatic Sleep Staging: A comparative study of state-of-the-art deep learning methods
Queen Mary University of London（伦敦玛丽女王大学）；Alan Turing Institute（艾伦·图灵研究所）；University of Lübeck（吕贝克大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Adelaide（阿德莱德大学）
L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging
Amazon Alexa（亚马逊Alexa）；Aarhus University（奥胡斯大学）；KU Leuven（荷语鲁汶大学）；Lausanne University Hospital (CHUV)（洛桑大学医院）；University of Lausanne（洛桑大学）；University of Oxford（牛津大学）；University of Lübeck（吕贝克大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；The University of Adelaide（阿德莱德大学）；Queen Mary University of London（伦敦玛丽女王大学）；Alan Turing Institute（艾伦·图灵研究所）
AnySleep: a channel-agnostic deep learning system for high-resolution sleep staging in multi-center cohorts
Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences（医学工程与技术学系，亚琛应用科学大学）；Department of Information and Computing Sciences, Utrecht University（信息与计算科学系，乌得勒支大学）；Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences（数据驱动技术研究所，亚琛应用科学大学）
A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input
Eindhoven University of Technology（埃因霍温理工大学）；Philips Sleep and Respiratory Care（飞利浦睡眠与呼吸护理部门）；Sleep Medicine Centre Kempenhaeghe（基姆彭海格睡眠医学中心）；Ulm University（乌尔姆大学）
Comparative Analysis of State-of-the-Art Foundation Models for Sleep Analysis Under Channel Reduction
University of Turku（图尔库大学）；Turku University Hospital（图尔库大学医院）；University College Cork（科克大学）；University at Albany, SUNY（纽约州立大学奥尔巴尼分校）；Fudan University（复旦大学）；University of Sydney（悉尼大学）
Fully-automated sleep staging: multicenter validation of a generalizable deep neural network for Parkinson's disease and isolated REM sleep behavior disorder
Aarhus University（奥胡斯大学）；Aarhus University Hospital（奥胡斯大学医院）；Lundbeck Foundation Parkinson’s Disease Research Center (PACE)（伦德贝克基金会帕金森病研究中心（PACE））；University of Cologne（科隆大学）；University Hospital Cologne（科隆大学医院）；Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne（科隆大学医学院暨科隆大学医院生物医学信息学研究所）；Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne（科隆大学医学院暨科隆大学医院科隆分子医学中心（CMMC））；Research Center Juelich（于利希研究中心）；University of Bonn（波恩大学）；University Hospital Bonn（波恩大学医院）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心（DZNE））；Glostrup University Hospital（格洛斯楚普大学医院）；Danish Center for Sleep Medicine（丹麦睡眠医学中心）
Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers
Faculty of Biomedical Engineering, Technion Institute of Technology（以色列理工学院 生物医学工程学院）；Faculty of Electrical Engineering, Technion Institute of Technology（以色列理工学院 电气工程学院）；Río Hortega University Hospital Valladolid（巴利亚多利德里奥奥尔塔加大学医院）；Biomedical Engineering Group, University of Valladolid（巴利亚多利德大学 生物医学工程组）；Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN)（生物工程、生物材料与纳米医学网络生物医学研究中心（CIBER-BBN））
