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 convolutional neural network for feature extraction and detection of sleep apnea events. The proposed method exhibits an accuracy of 99.56% and a sensitivity of 96.05%. This model outperforms several lower resolution state-of-the-art apnea detection methods. The complexity of the proposed model is analyzed. We also analyze the feasibility of model pruning and binarization to reduce the resource requirements on a wearable IoT device. The pruned model with 80\% sparsity exhibited an accuracy of 97.34% and a sensitivity of 86.48%. The binarized model exhibited an accuracy of 75.59% and sensitivity of 63.23%. The performance of low complexity patient-specific models derived from the generic model is also studied to analyze the feasibility of retraining existing models to fit patient-specific requirements. The patient-specific models on average exhibited an accuracy of 97.79% and sensitivity of 92.23%. The source code for this work is made publicly available.
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…
Polysomnography (PSG) is the gold standard for diagnosing sleep obstructive apnea (OSA). It allows monitoring of breathing events throughout the night. The detection of these events is usually done by trained sleep experts. However, this task is tedious, highly time-consuming and subject to important inter-scorer variability. In this study, we adapted our state-of-the-art deep learning method for sleep event detection, DOSED, to the detection of sleep breathing events in PSG for the diagnosis of OSA. We used a dataset of 52 PSG recordings with apnea-hypopnea event scoring from 5 trained sleep …
Sleep apnea is a common respiratory disorder characterized by breathing pauses during the night. Consequences of untreated sleep apnea can be severe. Still, many people remain undiagnosed due to shortages of hospital beds and trained sleep technicians. To assist in the diagnosis process, automated detection methods are being developed. Recent works have demonstrated that deep learning models can extract useful information from raw respiratory data and that such models can be used as a robust sleep apnea detector. However, trained sleep technicians take into account multiple sensor signals when…
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…
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）
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 inexpen…
Obstructive sleep apnea is a serious condition causing a litany of health problems especially in the pediatric population. However, this chronic condition can be treated if diagnosis is possible. The gold standard for diagnosis is an overnight sleep study, which is often unobtainable by many potentially suffering from this condition. Hence, we attempt to develop a fast non-invasive diagnostic tool by training a classifier on 2D and 3D facial images of a patient to recognize facial features associated with obstructive sleep apnea. In this comparative study, we consider both persistent homology …
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…
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 …
University of Oslo（奥斯陆大学）；National University of Singapore（新加坡国立大学）
Supervised machine learning applications in the health domain often face the problem of insufficient training datasets. The quantity of labelled data is small due to privacy concerns and the cost of data acquisition and labelling by a medical expert. Furthermore, it is quite common that collected data are unbalanced and getting enough data to personalize models for individuals is very expensive or even infeasible. This paper addresses these problems by (1) designing a recurrent Generative Adversarial Network to generate realistic synthetic data and to augment the original dataset, (2) enabling…
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…
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）
University of Minnesota（明尼苏达大学）；Nanyang Technological University（南洋理工大学）；Fairview Health（费尔维尤医疗）
Sleep plays a vital role in human health, both mental and physical. Sleep disorders like sleep apnea are increasing in prevalence, with the rapid increase in factors like obesity. Sleep apnea is most commonly treated with Continuous Positive Air Pressure (CPAP) therapy. Presently, however, there is no mechanism to monitor a patient's progress with CPAP. Accurate detection of sleep stages from CPAP flow signal is crucial for such a mechanism. We propose, for the first time, an automated sleep staging model based only on the flow signal. Deep neural networks have recently shown high accuracy on …
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…
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）
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…
In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database. Our model structure is composed of multiple dense convolutional units (DCU) followed by a bidirectional long-short term memory (LSTM) layer followed by a softmax output layer. The sleep events including sleep stages, arousal regions and multiple types of apnea and hypopnea are manually annotated by experts which enables us to train our proposed netw…
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）
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）
Introduction: Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. It is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). Methods: We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the r…
University of Twente（特文特大学）；Massachusetts General Hospital（麻省总医院）；Georgia Institute of Technology（佐治亚理工学院）；Deaconess Medical Center（迪肯尼斯医疗中心）
The gold standard to assess respiration during sleep is polysomnography; a technique that is burdensome, expensive (both in analysis time and measurement costs), and difficult to repeat. Automation of respiratory analysis can improve test efficiency and enable accessible implementation opportunities worldwide. Using 9,656 polysomnography recordings from the Massachusetts General Hospital (MGH), we trained a neural network (WaveNet) based on a single respiratory effort belt to detect obstructive apnea, central apnea, hypopnea and respiratory-effort related arousals. Performance evaluation inclu…
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）
Background and Aim: Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc. Sleep-related diseases could be diagnosed using Convolutional Neural Network Classifier. However, this classifier has not been successfully implemented into sleep stage classification systems due to high complexity and low accuracy of classification. The aim of this research is to increase the accuracy and reduce the learning time of Convolutional Neural Network Classifier. Methodology: The proposed system used a modified O…
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）
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）
Background: Despite the tremendous progress recently made towards automatic sleep staging in adults, it is currently unknown if the most advanced algorithms generalize to the pediatric population, which displays distinctive characteristics in overnight polysomnography (PSG). Methods: To answer the question, in this work, we conduct a large-scale comparative study on the state-of-the-art deep learning methods for pediatric automatic sleep staging. Six different deep neural networks with diverging features are adopted to evaluate a sample of more than 1,200 children across a wide spectrum of obs…
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）
The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called "sleeping cells", which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making them difficult to discover. To detect this kind of failure, we propose a Machine Learning (ML) framework based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the…
The rapidly-advancing technology of deep learning (DL) into the world of the Internet of Things (IoT) has not fully entered in the fields of m-Health yet. Among the main reasons are the high computational demands of DL algorithms and the inherent resource-limitation of wearable devices. In this paper, we present initial results for two deep learning architectures used to diagnose and analyze sleep patterns, and we compare them with a previously presented hand-crafted algorithm. The algorithms are designed to be reliable for consumer healthcare applications and to be integrated into low-power w…
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）
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…
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）
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）
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and …
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…
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）
