The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables. We exploit these changes with the aim of classifying the sleep stages in awake or asleep using pulse oximeter signals. We applied a recurrent neural network to heart rate and peripheral oxygen saturation signals to classify the sleep stage every 30 seconds. The network architecture consists of two stacked layers of bidirectional gated recurrent units (GRUs) and a softmax layer to classify the output. In this paper, we used 5000 patients from the Sleep Heart Health Study dataset. 2500 patients were used to train the network, and two subsets of 1250 were used to validate and test the trained models. In the test stage, the best result obtained was 90.13% accuracy, 94.13% sensitivity, 80.26% specificity, 92.05% precision, and 84.68% negative predictive value. Further, the Cohen's Kappa coefficient was 0.74 and the average absolute error percentage to the actual sleep time was 8.9%. The performance of the proposed network is comparable with the state-of-the-art algorithms when they use much more informative signals (except those with EEG).
Sleep disorders are very widespread in the world population and suffer from a generalized underdiagnosis, given the complexity of their diagnostic methods. Therefore, there is an increasing interest in developing simpler screening methods. A pulse oximeter is an ideal device for sleep disorder screenings since it is a portable, low-cost and accessible technology. This device can provide an estimation of the heart rate (HR), which can be useful to obtain information regarding the sleep stage. In this work, we developed a network architecture with the aim of classifying the sleep stage in awake …
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））
Objective: Chronic obstructive pulmonary disease (COPD) is a highly prevalent chronic condition. COPD is a major source of morbidity, mortality and healthcare costs. Spirometry is the gold standard test for a definitive diagnosis and severity grading of COPD. However, a large proportion of individuals with COPD are undiagnosed and untreated. Given the high prevalence of COPD and its clinical importance, it is critical to develop new algorithms to identify undiagnosed COPD, especially in specific groups at risk, such as those with sleep disorder breathing. To our knowledge, no research has look…
A Single Channel-Based Neonatal Sleep-Wake Classification using Hjorth Parameters and Improved Gradient Boosting
Department of Computer Science, Loyola University（洛约拉大学计算机科学系）；Department of Computer Science, University of People（人民大学计算机科学系）；Department of Electronic, Electrical and Systems Engineering, University of Birmingham（伯明翰大学电子、电气与系统工程系）；School of Computing, Engineering and the Built Environment, Edinburgh Napier University（爱丁堡纳皮尔大学计算、工程与环境科学学院）；RIOTU Lab, Prince Sultan University（普森大学RIOTU实验室）
Sleep state classification is vital in managing and understanding sleep patterns and is generally the first step in identifying acute or chronic sleep disorders. However, it is essential to do this without affecting the natural environment or conditions of the subject during their sleep. Techniques such as Polysomnography(PSG) are obtrusive and are not convenient for regular sleep monitoring. Fortunately, The rise of novel technologies and advanced computing has given a recent resurgence to monitoring sleep techniques. One such contactless and unobtrusive monitoring technique is Ballistocradio…
University of North Carolina at Greensboro（北卡罗来纳大学格林斯伯勒分校）；National Taiwan Normal University（台湾师范大学）；Chang Gung Memorial Hospital（长庚纪念医院）；Chang Gung University（长庚大学）；Duke University（杜克大学）；National Center for Theoretical Sciences（国家理论科学研究中心）
Persistent homology (PH) is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply PH to study time series; particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects -- {the PH's and hence persistence diagrams of its} sub-level set and Taken's lag map. Second, we propose a systematic {and computationally…
The most common reason for students' tardiness is due to waking up late every morning. The causes may vary from lack of rest and sleep, to failure of ascertaining an alarm clock's noise or sometimes, falling back to sleep again after snoozing an alarm. This paper's purpose is to address the problem concerning student's inability to continue to stay awake after turning off their alarms using Arduino-based pulse rate triggered alarm interrupt (APRTAI). The proposed system will enable students to make use of a higher pulse rate from their thumb to turn off an alarm which can be achieved by engagi…
Sleep-wake cycle detection is a key step when extrapolating sleep patterns from actigraphy data. Numerous supervised detection algorithms have been developed with parameters estimated from and optimized for a particular dataset, yet their generalizability from sensor to sensor or study to study is unknown. In this paper, we propose and validate an unsupervised algorithm -- CircaCP -- to detect sleep-wake cycles from minute-by-minute actigraphy data. It first uses a robust cosinor model to estimate circadian rhythm, then searches for a single change point (CP) within each cycle. We used CircaCP…
Classification of Infant Sleep/Wake States: Cross-Attention among Large Scale Pretrained Transformer Networks using Audio, ECG, and IMU Data
University of Illinois at Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校）；Beckman Institute for Advanced Science and Technology（贝克曼高级科学技术研究院）；Worcester Polytechnic Institute（伍斯特理工学院）
Deep Learning-based Automated Diagnosis of Obstructive Sleep Apnea and Sleep Stage Classification in Children Using Millimeter-wave Radar and Pulse Oximeter
University of Zurich（苏黎世大学）；ETH Zurich（苏黎世联邦理工学院）；Inselspital, Bern University Hospital（伯尔尼大学医院）；University of Bern（伯尔尼大学）
Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30-s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the l…
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）
This paper proposes a deep learning (DL) model for automatic sleep stage classification based on single-channel EEG data. The DL model features a convolutional neural network (CNN) and transformers. The model was designed to run on energy and memory-constrained devices for real-time operation with local processing. The Fpz-Cz EEG signals from a publicly available Sleep-EDF dataset are used to train and test the model. Four convolutional filter layers were used to extract features and reduce the data dimension. Then, transformers were utilized to learn the time-variant features of the data. To …
Sleep is among the most important factors affecting one's daily performance, well-being, and life quality. Nevertheless, it became possible to measure it in daily life in an unobtrusive manner with wearable devices. Rather than camera recordings and extraction of the state from the images, wrist-worn devices can measure directly via accelerometer, heart rate, and heart rate variability sensors. Some measured features can be as follows: time to bed, time out of bed, bedtime duration, minutes to fall asleep, and minutes after wake-up. There are several studies in the literature regarding sleep q…
Osaka University（大阪大学）；Nara Institute of Science and Technology（奈良先端科学技术大学院大学）；Fudan University（复旦大学）；Waseda University（早稻田大学）
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated…
Obstructive sleep Apnea (OSA) is a form of sleep disordered breathing characterized by frequent episodes of upper airway collapse during sleep. Pediatric OSA occurs in 1-5% of children and can related to other serious health conditions such as high blood pressure, behavioral issues, or altered growth. OSA is often diagnosed by studying the patient's sleep cycle, the pattern with which they progress through various sleep states such as wakefulness, rapid eye-movement, and non-rapid eye-movement. The sleep state data is obtained using an overnight polysomnography test that the patient undergoes …
In this paper, we first present a single-input, multiple-output convolutional neural network that can estimate both heart rate and respiration rate simultaneously by exploiting the underlying link between heart rate and respiration rate. The inputs to the neural network are the amplitude and phase of channel state information collected by a pair of WiFi devices. Our WiFi-based technique addresses privacy concerns and is adaptable to a variety of settings. This system overall accuracy for the heart and respiration rate estimation can reach 99.109% and 98.581%, respectively. Furthermore, we deve…
With the advancements in underwater wireless communications, internet of underwater things (IoUT) realization is inevitable to enable many practical applications, such as exploring ocean resources, ocean monitoring, underwater navigation, and surveillance. The IoUT network comprises battery-operated sensor nodes, and replacing or charging such batteries is challenging due to the harsh ocean environment. Hence, an energy-efficient IoUT network development becomes vital to improve the network lifetime. Therefore, this paper proposes unmanned aerial vehicle (UAV)-aided energy-efficient wake-up de…
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）
