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）
Stein Kristiansen, Konstantinos Nikolaidis, Thomas Plagemann, Vera Goebel, Gunn Marit Traaen, Britt Øverland, Lars Aakerøy, Tove-Elizabeth Hunt, Jan Pål Loennec… 展开作者
Stein Kristiansen, Konstantinos Nikolaidis, Thomas Plagemann, Vera Goebel, Gunn Marit Traaen, Britt Øverland, Lars Aakerøy, Tove-Elizabeth Hunt, Jan Pål Loennechen, Sigurd Loe Steinshamn, Christina Holt Bendz, Ole-Gunnar Anfinsen, Lars Gullestad, Harriet Akre
Sleep apnea is a serious and severely under-diagnosed sleep-related respiration disorder characterized by repeated disrupted breathing events during sleep. It is diagnosed via polysomnography which is an expensive test conducted in a sleep lab requiring sleep experts to manually score the recorded data. Since the symptoms of sleep apnea are often ambiguous, it is difficult for a physician to decide whether to prescribe polysomnography. In this study, we investigate whether helpful information can be obtained by collecting and automatically analysing sleep data using a smartphone and an inexpensive strain gauge respiration belt. We evaluate how accurately we can detect sleep apnea with wide variety of machine learning techniques with data from a clinical study with 49 overnight sleep recordings. With less than one hour of training, we can distinguish between normal and apneic minutes with an accuracy, sensitivity, and specificity of 0.7609, 0.7833, and 0.7217, respectively. These results can be achieved even if we train only on high-quality data from an entirely separate, clinically certified sensor, which has the potential to substantially reduce the cost of data collection. Data from a complete night can be analyzed in about one second on a smartphone.
University College Dublin（都柏林大学）；Think Biosolution（Think Biosolution 公司）
The abnormal pause or rate reduction in breathing is known as the sleep-apnea hypopnea syndrome and affects the quality of sleep of an individual. A novel method for the detection of sleep apnea events (pause in breathing) from peripheral oxygen saturation (SpO2) signals obtained from wearable devices is discussed in this paper. The paper details an apnea detection algorithm of a very high resolution on a per-second basis for which a 1-dimensional convolutional neural network -- which we termed SomnNET -- is developed. This network exhibits an accuracy of 97.08% and outperforms several lower r…
With recent advancements in deep learning methods, automatically learning deep features from the original data is becoming an effective and widespread approach. However, the hand-crafted expert knowledge-based features are still insightful. These expert-curated features can increase the model's generalization and remind the model of some data characteristics, such as the time interval between two patterns. It is particularly advantageous in tasks with the clinically-relevant data, where the data are usually limited and complex. To keep both implicit deep features and expert-curated explicit fe…
Internet of Things (IoT) enabled wearable sensors for health monitoring are widely used to reduce the cost of personal healthcare and improve quality of life. The sleep apnea-hypopnea syndrome, characterized by the abnormal reduction or pause in breathing, greatly affects the quality of sleep of an individual. This paper introduces a novel method for apnea detection (pause in breathing) from electrocardiogram (ECG) signals obtained from wearable devices. The novelty stems from the high resolution of apnea detection on a second-by-second basis, and this is achieved using a 1-dimensional convolu…
Identifying sleep problem severity from overnight polysomnography (PSG) recordings plays an important role in diagnosing and treating sleep disorders such as the Obstructive Sleep Apnea (OSA). This analysis traditionally is done by specialists manually through visual inspections, which can be tedious, time-consuming, and is prone to subjective errors. One of the solutions is to use Convolutional Neural Networks (CNN) where the convolutional and pooling layers behave as feature extractors and some fully-connected (FCN) layers are used for making final predictions for the OSA severity. In this p…
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）
Differentiating patients with obstructive sleep apnea from healthy controls based on heart rate - blood pressure coupling quantified by entropy-based indices
Gdańsk University of Technology（格但斯克工业大学）；Universidad Miguel Hernández（米格尔·埃尔南德斯大学）；Medical University of Gdańsk（格但斯克医科大学）
Massachusetts General Hospital（麻省总医院）；University of Zurich（苏黎世大学）；Washington University School of Medicine in St. Louis（圣路易斯华盛顿大学医学院）；MyAir Inc（MyAir公司）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Objective: Sleep related respiratory abnormalities are typically detected using polysomnography. There is a need in general medicine and critical care for a more convenient method to automatically detect sleep apnea from a simple, easy-to-wear device. The objective is to automatically detect abnormal respiration and estimate the Apnea-Hypopnea-Index (AHI) with a wearable respiratory device, compared to an SpO2 signal or polysomnography using a large (n = 412) dataset serving as ground truth. Methods: Simultaneously recorded polysomnographic (PSG) and wearable respiratory effort data were used …
Brown University（布朗大学）；Ohio State University（俄亥俄州立大学）；University of Alberta（阿尔伯塔大学）；C. H. Robinson（C.H.罗宾逊公司）；Wilfrid Laurier University（威尔弗里德·劳雷尔大学）
Pediatric obstructive sleep apnea affects an estimated 1-5% of elementary-school aged children and can lead to other detrimental health problems. Swift diagnosis and treatment are critical to a child's growth and development, but the variability of symptoms and the complexity of the available data make this a challenge. We take a first step in streamlining the process by focusing on inexpensive data from questionnaires and craniofacial measurements. We apply correlation networks, the Mapper algorithm from topological data analysis, and singular value decomposition in a process of exploratory d…
Obstructive sleep apnea (OSA) is one of the most widespread respiratory diseases today. Complete or relative breathing cessations due to upper airway subsidence during sleep is OSA. It has confirmed potential influence on Covid-19 hospitalization and mortality, and is strongly associated with major comorbidities of severe Covid-19 infection. Un-diagnosed OSA may also lead to a variety of severe physical and mental side-effects. To score OSA severity, nocturnal sleep monitoring is performed under defined protocols and standards called polysomnography (PSG). This method is time-consuming, expens…
Udine University Hospital（乌迪内大学医院）；University of Udine（乌迪内大学）
Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in stroke patients, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG). Unfortunatel…
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）
Multi-level Phenotypic Models of Cardiovascular Disease and Obstructive Sleep Apnea Comorbidities: A Longitudinal Wisconsin Sleep Cohort Study
North Carolina A&T State University（北卡罗来纳农工州立大学）；University of South Florida（南佛罗里达大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
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）
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）
Sleep posture analysis is widely used for clinical patient monitoring and sleep studies. Earlier research has revealed that sleep posture highly influences symptoms of diseases such as apnea and pressure ulcers. In this study, we propose a robust deep learning model capable of accurately detecting subjects and their sleeping postures using the publicly available data acquired from a commercial pressure mapping system. A combination of loss functions is used to discriminate subjects and their sleeping postures simultaneously. The experimental results show that our proposed method can identify t…
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）
Technical University of Denmark（丹麦技术大学）；Stanford University（斯坦福大学）；University Hospital of Copenhagen（哥本哈根大学医院）
Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individ…
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
Cedars Sinai Medical Center（西达赛奈医疗中心）；University of Pennsylvania（宾夕法尼亚大学）；Fox Chase Cancer Center（福克斯蔡斯癌症中心）；University of Kansas（堪萨斯大学）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Charite University Hospital（夏里特大学医院）；Federal University of Sao Paulo（圣保罗联邦大学）；Landspitali University Hospital（兰德斯皮塔利大学医院）；University of Iceland（冰岛大学）；University of Sydney（悉尼大学）；Sir Charles Gairdner Hospital（查尔斯·盖尔德纳爵士医院）；University of Western Australia（西澳大利亚大学）；Linkou Chang Gung Memorial Hospital（林口长庚纪念医院）；Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医学中心）
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）
An Attentive Dual-Encoder Framework Leveraging Multimodal Visual and Semantic Information for Automatic OSAHS Diagnosis
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（上海工程技术大学电子与电气工程学院）；INF Technology (shanghai) Co., Ltd.（INF科技（上海）有限公司）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）
University of Oslo（奥斯陆大学）；National University of Singapore（新加坡国立大学）；Oslo University Hospital Rikshospitalet（奥斯陆大学医院国家医院）；Lovisenberg Diakonale Hospital（洛维森贝格迪亚科纳勒医院）；Faculty of Medicine, University of Oslo（奥斯陆大学医学院）；Oslo University Hospital, Rikshospitalet（奥斯陆大学医院国家医院）；Norwegian University of Science and Technology（挪威科技大学）
Good training data is a prerequisite to develop useful ML applications. However, in many domains existing data sets cannot be shared due to privacy regulations (e.g., from medical studies). This work investigates a simple yet unconventional approach for anonymized data synthesis to enable third parties to benefit from such private data. We explore the feasibility of learning implicitly from unrealistic, task-relevant stimuli, which are synthesized by exciting the neurons of a trained deep neural network (DNN). As such, neuronal excitation serves as a pseudo-generative model. The stimuli data i…
