Patients with sleep disorders can better manage their lifestyle if they know about their special situations. Detection of such sleep disorders is usually possible by analyzing a number of vital signals that have been collected from the patients. To simplify this task, a number of Automatic Sleep Stage Recognition (ASSR) methods have been proposed. Most of these methods use temporal-frequency features that have been extracted from the vital signals. However, due to the non-stationary nature of sleep signals, such schemes are not leading an acceptable accuracy. Recently, some ASSR methods have been proposed which use deep neural networks for unsupervised feature extraction. In this paper, we proposed to combine the two ideas and use both temporal-frequency and unsupervised features at the same time. To augment the time resolution, each standard epoch is segmented into 5 sub-epochs. Additionally, to enhance the accuracy, we employ three classifiers with different properties and then use an ensemble method as the ultimate classifier. The simulation results show that the proposed method enhances the accuracy of conventional ASSR methods.
ULW-SleepNet: An Ultra-Lightweight Network for Multimodal Sleep Stage Scoring
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；Faculty of Information Technology, University of Jyväskylä（于韦斯屈莱大学信息科技学院）；Key Laboratory of Social Computing and Cognitive Intelligence, Ministry of Education（教育部社会计算与认知智能重点实验室）；School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology（大连理工大学生物医学工程学院）；Department of Computing Sciences, Tampere University（塔尔库大学计算机科学系）；Faculty of Medicine, University of Helsinki（赫尔辛基大学医学学院）
Addressing database variability in learning from medical data: an ensemble-based approach using convolutional neural networks and a case of study applied to automatic sleep scoring
Haaglanden Medisch Centrum（哈格兰登医疗中心）；University of A Coruña（拉科鲁尼亚大学）
Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review
University of Bern（伯尔尼大学）；University of Applied Sciences and Arts of Southern Switzerland（瑞士南部应用科学与艺术大学）；Inselspital, Bern University Hospital（伯尔尼大学医院）；Ohio Sleep Medicine Institute（俄亥俄睡眠医学研究所）
Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
Department of Computer Science, Reykjavik University（雷克雅未克大学计算机科学系）；Reykjavik University Sleep Institute（雷克雅未克大学睡眠研究所）；Reykjavik University（雷克雅未克大学）；Department of Engineering, Reykjavik University（雷克雅未克大学工程系）；School of Mathematical Sciences, University of Southampton（萨塞克斯大学数学科学学院）
NeuroSleepNet: An Explainable Multi-Head Attention-Based Framework With Spatial and Multi-Scale Independent Temporal Context Learning for Automatic Sleep Stage Scoring
Rajshahi University of Engineering & Technology（拉杰沙希工程技术大学）；Deakin University（迪肯大学）；Charité University Hospital（夏里特大学医院）；The University of Melbourne（墨尔本大学）
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）
Multitaper Spectral Estimation HDP-HMMs for EEG Sleep Inference
MIT Department of BCS（麻省理工学院脑与认知科学系）；MIT Department of EECS（麻省理工学院电子工程与计算机科学系）；Harvard-MIT HST（哈佛-麻省理工健康科学与技术项目）；SEAS Department of Harvard（哈佛大学工程与应用科学学院）；MGH Department of Anesthesia, Critical Care and Pain Medicine（麻省总医院麻醉、重症监护与疼痛医学科）
Validation of a new, minimally-invasive, software smartphone device to predict sleep apnea and its severity: transversal study
Université Paris Cité（巴黎西岱大学）；APHP, Hôpital Bichat（巴黎公共医疗救助机构，比沙医院）；Mitral (Apneal)（Mitral（Apneal）公司）；Université Paris-Est（巴黎东大学）；APHP, Hôpital Henri Mondor（巴黎公共医疗救助机构，亨利·蒙多尔医院）；APHP, Mondor University Hospitals（巴黎公共医疗救助机构，蒙多尔大学医院）；Université Paris Est Créteil（巴黎东克雷泰伊大学）；Centre ChronoS, GHU Paris - Psychiatry & Neurosciences（巴黎精神病学与神经科学大学医院集团，ChronoS中心）；APHP, Hôpital Bichat Claude Bernard（巴黎公共医疗救助机构，比沙-克洛德·贝尔纳医院）；CEREMADE, CNRS-UMR 7534, Université Paris-Dauphine PSL（巴黎第九大学PSL，CEREMADE，法国国家科学研究中心UMR 7534）；Digital Medical Hub SAS, Assistance Publique Hôpitaux de Paris AP-HP（巴黎公共医疗救助机构，数字医疗中心SAS）
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）
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）
MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification
Centre for Computational Science and Mathematical Modelling, Coventry University（计算科学与数学建模中心，科文大学）；Institute for Infocomm Research, A*STAR（信息通信研究所，A*STAR）
Self-supervised EEG Representation Learning for Automatic Sleep Staging
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Relativity（Relativity公司）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Universidad Carlos III de Madrid（马德里卡洛斯三世大学）；Instituto de Investigación Sanitaria Gregorio Marañón（格雷戈里奥·马拉尼翁健康研究所）；Hospital Universitario Severo Ochoa（塞维罗·奥乔亚大学医院）；IRBLleida-Hospital Universitari Santa Maria Lleida（莱里达生物医学研究所-圣玛丽亚大学医院莱里达分院）；IRBLleida-Hospital Universitari Arnau de Vilanova and Santa Maria（莱里达生物医学研究所-阿诺·德·维拉诺瓦与圣玛丽亚大学医院）
