In recent years, deep learning has shown potential and efficiency in a wide area including computer vision, image and signal processing. Yet, translational challenges remain for user applications due to a lack of interpretability of algorithmic decisions and results. This black box problem is particularly problematic for high-risk applications such as medical-related decision-making. The current study goal was to design an interpretable deep learning system for time series classification of electroencephalogram (EEG) for sleep stage scoring as a step toward designing a transparent system. We have developed an interpretable deep neural network that includes a kernel-based layer based on a set of principles used for sleep scoring by human experts in the visual analysis of polysomnographic records. A kernel-based convolutional layer was defined and used as the first layer of the system and made available for user interpretation. The trained system and its results were interpreted in four levels from the microstructure of EEG signals, such as trained kernels and the effect of each kernel on the detected stages, to macrostructures, such as the transition between stages. The proposed system demonstrated greater performance than prior studies and the results of interpretation showed that the system learned information which was consistent with expert knowledge.
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）
Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning
Emory University（埃默里大学）；University Hospital Tübingen（图宾根大学医院）；University of Tübingen（图宾根大学）；Technische Universität Dresden（德累斯顿工业大学）；German Center for Neurodegenerative Diseases (DZNE)（德国神经退行性疾病中心）；FH Aachen University of Applied Sciences（亚琛应用技术大学）
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）
EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning
University of Applied Science and Technology（应用科技大学）；MZG Consulting（MZG咨询公司）；Institute of Electrical and Electronics Engineers（电气与电子工程师协会）；Baku State University（巴库国立大学）
Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal
University of A Coruna（拉科鲁尼亚大学）；Instituto de Investigacion Biomedica de A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所）；Complexo Hospitalario Universitario de A Coruña (CHUAC)（拉科鲁尼亚大学医院）
SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification
Queen Mary University of London（伦敦玛丽女王大学）；Alan Turing Institute（艾伦·图灵研究所）；Aarhus University（奥胡斯大学）；University of Bristol（布里斯托大学）；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（DFKI））；KU Leuven（鲁汶大学）
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）
SleepPPG-Net2: Deep learning generalization for sleep staging from photoplethysmography
The Faculty of Data and Decision Sciences, Technion, Israel Institute of Technology（以色列理工学院数据与决策科学学院）；Faculty of Biomedical Engineering, Technion, Israel Institute of Technology（以色列理工学院生物医学工程学院）
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）
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences（弗劳恩霍夫亚琛应用科学大学医学工程与技术数学系）；Department of Information and Computing Sciences, Utrecht University（乌得勒支大学信息与计算科学系）；Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences（弗劳恩霍夫亚琛应用科学大学数据驱动技术研究所）
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）
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）
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）
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）
Assessing the Real-World Utility of Explainable AI for Arousal Diagnostics: An Application-Grounded User Study
IT-Designers Gruppe Esslingen am Neckar（埃斯林根内卡尔IT设计组）；University of Tübingen（图宾根大学）；University of Giessen（吉森大学）；Klinikum Esslingen, Klinik für Kardiologie, Pneumologie und Angilologie（埃斯林根克林克医院，心内科、呼吸科和内科）；Technical University of Munich（慕尼黑技术大学）
