Research

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20 pages, 6202 KiB

Open AccessArticle

A Vision-Based System for In-Sleep Upper-Body and Head Pose Classification

by Yan-Ying Li, Shoue-Jen Wang and Yi-Ping Hung

Sensors 2022, 22(5), 2014; https://0-doi-org.brum.beds.ac.uk/10.3390/s22052014 - 04 Mar 2022

Cited by 6 | Viewed by 3142

Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and [...] Read more.

Sleep quality is known to have a considerable impact on human health. Recent research shows that head and body pose play a vital role in affecting sleep quality. This paper presents a deep multi-task learning network to perform head and upper-body detection and pose classification during sleep. The proposed system has two major advantages: first, it detects and predicts upper-body pose and head pose simultaneously during sleep, and second, it is a contact-free home security camera-based monitoring system that can work on remote subjects, as it uses images captured by a home security camera. In addition, a synopsis of sleep postures is provided for analysis and diagnosis of sleep patterns. Experimental results show that our multi-task model achieves an average of 92.5% accuracy on challenging datasets, yields the best performance compared to the other methods, and obtains 91.7% accuracy on the real-life overnight sleep data. The proposed system can be applied reliably to extensive public sleep data with various covering conditions and is robust to real-life overnight sleep data. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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16 pages, 1293 KiB

Open AccessArticle

Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network

by Cheng-Yu Yeh, Hung-Yu Chang, Jiy-Yao Hu and Chun-Cheng Lin

Sensors 2022, 22(2), 510; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020510 - 10 Jan 2022

Cited by 8 | Viewed by 1878

Abstract

A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether [...] Read more.

A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether the reduction of lower frequency P and T waves can increase the accuracy of the detection of apnea events. This study proposed filter bank decomposition to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal. One-minute ECG signals obtained from the MIT PhysioNet Apnea-ECG database were used to train the CNN models and test the accuracy of detecting apnea events for different subbands. The results show that the use of the newly selected subject-independent datasets can avoid the overestimation of the accuracy of the apnea event detection and can test the difference in the accuracy of different subbands. The frequency band of 31.25–37.5 Hz can achieve 100% per-recording accuracy with 85.8% per-minute accuracy using the newly selected subject-independent datasets and is recommended as a promising subband of ECG signals that can cooperate with the proposed 1D CNN model for the diagnosis of OSA. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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18 pages, 5089 KiB

Open AccessArticle

The Biomechanical Mechanism of Upper Airway Collapse in OSAHS Patients Using Clinical Monitoring Data during Natural Sleep

by Liujie Chen, Tan Xiao and Ching Tai Ng

Sensors 2021, 21(22), 7457; https://0-doi-org.brum.beds.ac.uk/10.3390/s21227457 - 10 Nov 2021

Cited by 1 | Viewed by 2119

Abstract

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep disorder characterized by repeated pharyngeal collapse with partial or complete obstruction of the upper airway. This study investigates the biomechanics of upper airway collapse of OSASH patients during natural sleep. Computerized tomography (CT) [...] Read more.

Obstructive sleep apnea hypopnea syndrome (OSAHS) is a common sleep disorder characterized by repeated pharyngeal collapse with partial or complete obstruction of the upper airway. This study investigates the biomechanics of upper airway collapse of OSASH patients during natural sleep. Computerized tomography (CT) scans and data obtained from a device installed on OSASH patients, which is comprised of micro pressure sensors and temperature sensors, are used to develop a pseudo three-dimensional (3D) finite element (FE) model of the upper airway. With consideration of the gravity effect on the soft palate while patients are in a supine position, a fluid–solid coupling analysis is performed using the FE model for the two respiratory modes, eupnea and apnea. The results of this study show that the FE simulations can provide a satisfactory representation of a patient’s actual respiratory physiological processes during natural sleep. The one-way valve effect of the soft palate is one of the important mechanical factors causing upper airway collapse. The monitoring data and FE simulation results obtained in this study provide a comprehensive understanding of the occurrence of OSAHS and a theoretical basis for the individualized treatment of patients. The study demonstrates that biomechanical simulation is a powerful supplementation to clinical monitoring and evaluation. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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19 pages, 4078 KiB

Open AccessArticle

Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

by Catalin Dumitrescu, Ilona-Madalina Costea, Angel-Ciprian Cormos and Augustin Semenescu

Sensors 2021, 21(21), 7230; https://0-doi-org.brum.beds.ac.uk/10.3390/s21217230 - 30 Oct 2021

Cited by 5 | Viewed by 2064

Abstract

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling [...] Read more.

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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21 pages, 3623 KiB

Open AccessArticle

Enhanced Monitoring of Sleep Position in Sleep Apnea Patients: Smartphone Triaxial Accelerometry Compared with Video-Validated Position from Polysomnography

by Ignasi Ferrer-Lluis, Yolanda Castillo-Escario, Josep Maria Montserrat and Raimon Jané

Sensors 2021, 21(11), 3689; https://0-doi-org.brum.beds.ac.uk/10.3390/s21113689 - 26 May 2021

Cited by 7 | Viewed by 3467

Abstract

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, [...] Read more.

Poor sleep quality is a risk factor for multiple mental, cardiovascular, and cerebrovascular diseases. Certain sleep positions or excessive position changes can be related to some diseases and poor sleep quality. Nevertheless, sleep position is usually classified into four discrete values: supine, prone, left and right. An increase in sleep position resolution is necessary to better assess sleep position dynamics and to interpret more accurately intermediate sleep positions. This research aims to study the feasibility of smartphones as sleep position monitors by (1) developing algorithms to retrieve the sleep position angle from smartphone accelerometry; (2) monitoring the sleep position angle in patients with obstructive sleep apnea (OSA); (3) comparing the discretized sleep angle versus the four classic sleep positions obtained by the video-validated polysomnography (PSG); and (4) analyzing the presence of positional OSA (pOSA) related to its sleep angle of occurrence. Results from 19 OSA patients reveal that a higher resolution sleep position would help to better diagnose and treat patients with position-dependent diseases such as pOSA. They also show that smartphones are promising mHealth tools for enhanced position monitoring at hospitals and home, as they can provide sleep position with higher resolution than the gold-standard video-validated PSG. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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20 pages, 1457 KiB

Open AccessArticle

Which Are the Central Aspects of Infant Sleep? The Dynamics of Sleep Composites across Infancy

by Sarah F. Schoch, Reto Huber, Malcolm Kohler and Salome Kurth

Sensors 2020, 20(24), 7188; https://0-doi-org.brum.beds.ac.uk/10.3390/s20247188 - 15 Dec 2020

Cited by 15 | Viewed by 4148

Abstract

Sleep during infancy is important for the well-being of both infant and parent. Therefore, there is large interest in characterizing infant sleep with reliable tools, for example by combining actigraphy with 24-h-diaries. However, it is critical to select the right variables to characterize [...] Read more.

Sleep during infancy is important for the well-being of both infant and parent. Therefore, there is large interest in characterizing infant sleep with reliable tools, for example by combining actigraphy with 24-h-diaries. However, it is critical to select the right variables to characterize sleep. In a longitudinal investigation, we collected sleep data of 152 infants at ages 3, 6, and 12 months. Using principal component analysis, we identified five underlying sleep composites from 48 commonly-used sleep variables: Sleep Night, Sleep Day, Sleep Activity, Sleep Timing, and Sleep Variability. These composites accurately reflect known sleep dynamics throughout infancy as Sleep Day (representing naps), Sleep Activity (representing sleep efficiency and consolidation), and Sleep Variability (representing day-to-day stability) decrease across infancy, while Sleep Night (representing nighttime sleep) slightly increases, and Sleep Timing becomes earlier as one ages. We uncover interesting dynamics between the sleep composites and demonstrate that infant sleep is not only highly variable between infants but also dynamic within infants across time. Interestingly, Sleep Day is associated with behavioral development and therefore a potential marker for maturation. We recommend either the use of sleep composites or the core representative variables within each sleep composite for more reliable research. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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Review

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18 pages, 1029 KiB

Open AccessReview

Clinical and Research Solutions to Manage Obstructive Sleep Apnea: A Review

by Fen Xia and Mohamad Sawan

Sensors 2021, 21(5), 1784; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051784 - 04 Mar 2021

Cited by 17 | Viewed by 4772

Abstract

Obstructive sleep apnea (OSA), a common sleep disorder disease, affects millions of people. Without appropriate treatment, this disease can provoke several health-related risks including stroke and sudden death. A variety of treatments have been introduced to relieve OSA. The main present clinical treatments [...] Read more.

Obstructive sleep apnea (OSA), a common sleep disorder disease, affects millions of people. Without appropriate treatment, this disease can provoke several health-related risks including stroke and sudden death. A variety of treatments have been introduced to relieve OSA. The main present clinical treatments and undertaken research activities to improve the success rate of OSA were covered in this paper. Additionally, guidelines on choosing a suitable treatment based on scientific evidence and objective comparison were provided. This review paper specifically elaborated the clinically offered managements as well as the research activities to better treat OSA. We analyzed the methodology of each diagnostic and treatment method, the success rate, and the economic burden on the world. This review paper provided an evidence-based comparison of each treatment to guide patients and physicians, but there are some limitations that would affect the comparison result. Future research should consider the consistent follow-up period and a sufficient number of samples. With the development of implantable medical devices, hypoglossal nerve stimulation systems will be designed to be smart and miniature and one of the potential upcoming research topics. The transcutaneous electrical stimulation as a non-invasive potential treatment would be further investigated in a clinical setting. Meanwhile, no treatment can cure OSA due to the complicated etiology. To maximize the treatment success of OSA, a multidisciplinary and integrated management would be considered in the future. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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21 pages, 464 KiB

Open AccessEditor’s ChoiceReview

A Systematic Review of Sensing Technologies for Wearable Sleep Staging

by Syed Anas Imtiaz

Sensors 2021, 21(5), 1562; https://0-doi-org.brum.beds.ac.uk/10.3390/s21051562 - 24 Feb 2021

Cited by 75 | Viewed by 8618

Abstract

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), [...] Read more.

Designing wearable systems for sleep detection and staging is extremely challenging due to the numerous constraints associated with sensing, usability, accuracy, and regulatory requirements. Several researchers have explored the use of signals from a subset of sensors that are used in polysomnography (PSG), whereas others have demonstrated the feasibility of using alternative sensing modalities. In this paper, a systematic review of the different sensing modalities that have been used for wearable sleep staging is presented. Based on a review of 90 papers, 13 different sensing modalities are identified. Each sensing modality is explored to identify signals that can be obtained from it, the sleep stages that can be reliably identified, the classification accuracy of systems and methods using the sensing modality, as well as the usability constraints of the sensor in a wearable system. It concludes that the two most common sensing modalities in use are those based on electroencephalography (EEG) and photoplethysmography (PPG). EEG-based systems are the most accurate, with EEG being the only sensing modality capable of identifying all the stages of sleep. PPG-based systems are much simpler to use and better suited for wearable monitoring but are unable to identify all the sleep stages. Full article

(This article belongs to the Special Issue Advances in Sleep Monitoring Sensors, Devices, and Computational Technologies)

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