In this research, we attempt to answer the following basic research questions: Is a machine learning model able to classify all types of sleep disorders with high accuracy? Among the different modalities of sleep disorder signals, are some more important than others? Do raw signals improve the performance of a deep learning model when they are used as inputs? Prior research showed that most sleep disorders belong to eight categories. To study the performance of machine learning models in classifying polysomnography recordings into the eight categories of sleep pathologies, we selected the Cyclic Alternating Pattern Sleep Database. We developed a multi-channel Deep Learning model where a set of Convolutional Neural Networks were applied to six channels of raw signals of different modalities, including three channels of EEG signals and one channel each of EMG, ECG , and EOG signals. To compare the performance of the DL model with other models, we designed a model that took spectral features, instead of raw signals, as its inputs. We first studied the "importance" issue of signal modalities using the RF algorithm. We found that ECG contributed most to the important features and EMG second, among the four signal modalities. We then studied the accuracy performance of the proposed machine learning models. We verified that the multi-channel DL-R model, which took raw signals as its inputs, outperformed all other models, with its sensitivity and specificity scores both being above 95 %. This accuracy performance is on a par with those published results which dealt with fewer types of sleep disorders. We adopted two popular heatmap-generating techniques, with which we confirmed that the DL model's superior performance was owing to the CNN network's ability to extract potent features from raw signals.
UT-OSANet: A Multimodal Deep Learning model for Evaluating and Classifying Obstructive Sleep Apnea
South China University of Technology（华南理工大学）；Center for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical University（广州医科大学附属市八医院睡眠与昼夜医学中心）
Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram
Technical University of Denmark（丹麦技术大学）；Stanford University（斯坦福大学）；University Hospital Copenhagen（哥本哈根大学医院）；Telecom ParisTech（巴黎高等电信学院）；Universite Paris-Saclay（巴黎-萨克雷大学）；Dreem（Dreem公司）
SleepPPG-Net: a deep learning algorithm for robust sleep staging from continuous photoplethysmography
Technion-Israel Institute of Technology（以色列理工学院）；University of Cambridge（剑桥大学）；University of London（伦敦大学）；Mines ParisTech - PSL University（巴黎国立高等矿业学校-巴黎文理研究大学）
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）
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）
Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation
Charles Sturt University（查尔斯特大学）；Asia Pacific International College（亚太国际学院）；Worcester State University（伍斯特州立大学）；University of Kurdistan Hewler（库尔德斯坦埃尔比勒大学）
背景和目的：睡眠的每个阶段都会影响人体健康，任何阶段睡眠不足都可能导致睡眠障碍，如睡眠异常、呼吸暂停、失眠等。然而，由于分类的高复杂度和低准确度，该分类器尚未成功地实施到睡眠阶段分类系统中。本研究的目的是提高卷积神经网络分类器的分类精度，减少学习时间。方法学：所提出的系统使用修改的正交卷积神经网络和修改的亚当优化技术，以提高睡眠阶段分类的准确性，并减少由于S形激活函数而发生的梯度饱和问题。该系统使用Leaky Rectified Linear Unit（ReLU）代替sigmoid激活函数作为激活函数。结果如下：该系统被称为增强型睡眠阶段分类系统（ESSC），使用六个不同的数据库来训练和测试不同睡眠阶段的模型。这些数据库是都柏林大学数据库（UCD），贝斯以色列女执事医疗中心麻省理工学院数据库（MIT-BIH），睡眠欧洲数据格式（EDF），睡眠EDF扩展，蒙特利尔睡眠研究档案（MASS）和睡眠心脏健康研究（SHHS）。我们的结果表明，梯度饱和问题不再存在。修改后的亚当优化器有助于减少噪声，这反过来又导致更快的收敛时间。结论：与最先进的解决方案相比，ESSC的收敛速度加快，分类精度更高。
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）
Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Model
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore（南洋理工大学电子与电气工程学院）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院）；Nutanix, CA, USA（Nutanix公司）；Center for Machine Vision and Signal Analysis, University of Oulu, Oulu, Finland（奥卢大学机器视觉与信号分析中心）；Institute of Automation, Chinese Academy of Sciences, Beijing, China（中国科学院自动化研究所）
背景：脑电图（EEG）监测睡眠期间的大脑活动，并用于识别睡眠障碍。在睡眠医学中，临床医生在所谓的睡眠阶段解释原始EEG信号，这些睡眠阶段由专家分配给每30秒的信号窗口。对于诊断，他们还依赖于较短的原型微架构事件，这些事件表现出可变的持续时间和形状，例如纺锤体，K复合体或觉醒。注释这些事件传统上是由受过训练的睡眠专家执行的，使得该过程耗时、繁琐并且受到评分者间差异的影响。为了自动化这个过程，已经开发了各种方法，但是这些方法是特定于事件的，并且依赖于手工制作的特征的提取。新方法：我们提出了一种新的深度学习架构，称为Dreem One Shot Event Detector（DOSED）。DOSED联合预测EEG时间序列中事件的位置、持续时间和类型。本文所提出的方法应用于与睡眠相关的微架构事件，其灵感来自于为计算机视觉开发的对象检测器，如YOLO和SSD。它依赖于一个卷积神经网络，该网络从原始EEG信号中构建特征表示，以及分别执行定位和分类的两个模块。结果和与其他方法的比较：所提出的方法在4个数据集和3种类型的事件（纺锤波，K复合波，唤醒）上进行了测试，并与当前最先进的检测算法进行了比较。结论：结果表明，这种新方法的多功能性和改进的性能相比，目前国家的最先进的检测方法。
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）
Attentive Dilated Convolution for Automatic Sleep Staging using Force-directed Layout
Department of Electrical and Electronic Engineering, BRAC University（BRAC大学电气与电子工程系）；Department of Biomedical Engineering, Linköping University（林雪平大学生物医学工程系）；Department of Electrical and Computer Engineering, Texas A&M University（德克萨斯农工大学电气与计算机工程系）
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））
