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Sensors in Sleep Monitoring
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Sensors in Sleep Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 21079

Special Issue Editor

Dr. Jonathan Elliott

E-Mail Website
Guest Editor
School of Medicine Research Physiologist, VA Portland Health Care System, Oregon Health and Science University, Portland, OR, USA
Interests: sleep physiology and sleep-wake disturbances; post-traumatic stress disorder; exercise physiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The gold-standard assessment of sleep uses polysomnography with multiple sensors, capturing simultaneous signals to unambiguously stage sleep, identify sleep disorders, and ultimately characterize an overall sleep phenotype. However, polysomnography is not without limitations, many of which can be overcome or mitigated with wearables and other remote-based sensors that are home-based, non-intrusive, affordable, and capable of capturing continuous data across multiple nights. The importance of sleep in overall health is being increasingly recognized, reflected by the burgeoning technology sector aimed at both the consumer- and provider-level markets. This Special Issue will highlight new sleep sensors, alogrithms, and other novel approaches to advancing the recording of sleep.

Dr. Jonathan Elliott 
Guest Editor

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Keywords

  • sleep sensors
  • electroencephalography
  • electromyography
  • electrooculography
  • sleep-disordered breathing
  • sleep disorders

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Published Papers (7 papers)

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Research

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17 pages, 3372 KiB  
Article
Adoption of Transformer Neural Network to Improve the Diagnostic Performance of Oximetry for Obstructive Sleep Apnea
by Malak Abdullah Almarshad, Saad Al-Ahmadi, Md Saiful Islam, Ahmed S. BaHammam and Adel Soudani
Sensors 2023, 23(18), 7924; https://doi.org/10.3390/s23187924 - 15 Sep 2023
Cited by 6 | Viewed by 2686
Abstract
Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents [...] Read more.
Scoring polysomnography for obstructive sleep apnea diagnosis is a laborious, long, and costly process. Machine learning approaches, such as deep neural networks, can reduce scoring time and costs. However, most methods require prior filtering and preprocessing of the raw signal. Our work presents a novel method for diagnosing obstructive sleep apnea using a transformer neural network with learnable positional encoding, which outperforms existing state-of-the-art solutions. This approach has the potential to improve the diagnostic performance of oximetry for obstructive sleep apnea and reduce the time and costs associated with traditional polysomnography. Contrary to existing approaches, our approach performs annotations at one-second granularity. Allowing physicians to interpret the model’s outcome. In addition, we tested different positional encoding designs as the first layer of the model, and the best results were achieved using a learnable positional encoding based on an autoencoder with structural novelty. In addition, we tried different temporal resolutions with various granularity levels from 1 to 360 s. All experiments were carried out on an independent test set from the public OSASUD dataset and showed that our approach outperforms current state-of-the-art solutions with a satisfactory AUC of 0.89, accuracy of 0.80, and F1-score of 0.79. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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16 pages, 3754 KiB  
Article
A Customized Deep Sleep Recommender System Using Hybrid Deep Learning
by Ji-Hyeok Park and Jae-Dong Lee
Sensors 2023, 23(15), 6670; https://doi.org/10.3390/s23156670 - 25 Jul 2023
Cited by 2 | Viewed by 1930
Abstract
This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes [...] Read more.
This paper proposes a recommendation system based on a hybrid learning approach for a personal deep sleep service, called the Customized Deep Sleep Recommender System (CDSRS). Sleep is one of the most important factors for human life in modern society. Optimal sleep contributes to increasing work efficiency and controlling overall well-being. Therefore, a sleep recommendation service is considered a necessary service for modern individuals. Accurate sleep analysis and data are required to provide such a personalized sleep service. However, given the variations in sleep patterns between individuals, there is currently no international standard for sleep. Additionally, service platforms face a cold start problem when dealing with new users. To address these challenges, this study utilizes K-means clustering analysis to define sleep patterns and employs a hybrid learning algorithm to evaluate recommendations by combining user-based and collaborative filtering methods. It also incorporates feedback top-N classification processing for user profile learning and recommendations. The behavior of the study model is as follows. Using personal information received through mobile devices and data, such as snoring, sleep time, movement, and noise collected through AI motion beds, we recommend sleep and receive user evaluations of recommended sleep. This assessment reconstructs the profile and, finally, makes recommendations using top-N classification. The experimental results were evaluated using two absolute error measurement methods: mean squared error (MSE) and mean absolute percentage error (MAPE). The research results regarding the hybrid learning methods show 13.2% fewer errors than collaborative filtering (CF) and 10.2% fewer errors than content-based filtering (CBF) on an MSE basis. According to the MAPE, the methods are 14.7% more accurate than the CF model and 9.2% better than the CBF model. These results demonstrate that CDSRS systems can provide more accurate recommendations and customized sleep services to users than CF, CBF, and combination models. As a result, CDSRS, a hybrid learning method, can better reflect a user’s evaluation than traditional methods and can increase the accuracy of recommendations as the number of users increases. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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16 pages, 834 KiB  
Article
Validating Force Sensitive Resistor Strip Sensors for Cardiorespiratory Measurement during Sleep: A Preliminary Study
by Mostafa Haghi, Akhmadbek Asadov, Andrei Boiko, Juan Antonio Ortega, Natividad Martínez Madrid and Ralf Seepold
Sensors 2023, 23(8), 3973; https://doi.org/10.3390/s23083973 - 13 Apr 2023
Cited by 9 | Viewed by 2581
Abstract
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring [...] Read more.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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23 pages, 2650 KiB  
Article
Creation and Analysis of a Respiratory Sensor Using the Screen-Printing Method and the Arduino Platform
by Jarosław Wojciechowski and Ewa Skrzetuska
Sensors 2023, 23(4), 2315; https://doi.org/10.3390/s23042315 - 19 Feb 2023
Cited by 2 | Viewed by 3370
Abstract
The aim of this paper is to present novel highly sensitive and stretchable strain sensors using data analysis to report on human live parameters using the Arduino embedded system as a proof of concept in developing new and innovative solutions for health care. [...] Read more.
The aim of this paper is to present novel highly sensitive and stretchable strain sensors using data analysis to report on human live parameters using the Arduino embedded system as a proof of concept in developing new and innovative solutions for health care. The article introduces the solution of textile sensor origination with electrical resistance measurement using the mobile Arduino microcontroller in the designed/elaborated textile printed sensor. The textile sensor was developed by the screen printing technique based on the water dispersion of carbon nanotubes during printing composition. By stretching and squeezing the T-shirt during breathing, the electrical resistances of the printed sensor were changed. The measured resistance corresponded to the number of breaths of the person wearing the T-shirt. The microcontroller calculated the number of breaths as a number of electrical resistance peaks, which then led to monitoring human live parameters. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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15 pages, 2602 KiB  
Article
Automated Detection of Sleep Apnea-Hypopnea Events Based on 60 GHz Frequency-Modulated Continuous-Wave Radar Using Convolutional Recurrent Neural Networks: A Preliminary Report of a Prospective Cohort Study
by Jae Won Choi, Dong Hyun Kim, Dae Lim Koo, Yangmi Park, Hyunwoo Nam, Ji Hyun Lee, Hyo Jin Kim, Seung-No Hong, Gwangsoo Jang, Sungmook Lim and Baekhyun Kim
Sensors 2022, 22(19), 7177; https://doi.org/10.3390/s22197177 - 21 Sep 2022
Cited by 16 | Viewed by 3653
Abstract
Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave [...] Read more.
Radar is a promising non-contact sensor for overnight polysomnography (PSG), the gold standard for diagnosing obstructive sleep apnea (OSA). This preliminary study aimed to demonstrate the feasibility of the automated detection of apnea-hypopnea events for OSA diagnosis based on 60 GHz frequency-modulated continuous-wave radar using convolutional recurrent neural networks. The dataset comprised 44 participants from an ongoing OSA cohort, recruited from July 2021 to April 2022, who underwent overnight PSG with a radar sensor. All PSG recordings, including sleep and wakefulness, were included in the dataset. Model development and evaluation were based on a five-fold cross-validation. The area under the receiver operating characteristic curve for the classification of 1-min segments ranged from 0.796 to 0.859. Depending on OSA severity, the sensitivities for apnea-hypopnea events were 49.0–67.6%, and the number of false-positive detections per participant was 23.4–52.8. The estimated apnea-hypopnea index showed strong correlations (Pearson correlation coefficient = 0.805–0.949) and good to excellent agreement (intraclass correlation coefficient = 0.776–0.929) with the ground truth. There was substantial agreement between the estimated and ground truth OSA severity (kappa statistics = 0.648–0.736). The results demonstrate the potential of radar as a standalone screening tool for OSA. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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15 pages, 1484 KiB  
Article
Sleep Monitoring during Acute Stroke Rehabilitation: Toward Automated Measurement Using Multimodal Wireless Sensors
by Pin-Wei Chen, Megan K. O’Brien, Adam P. Horin, Lori L. McGee Koch, Jong Yoon Lee, Shuai Xu, Phyllis C. Zee, Vineet M. Arora and Arun Jayaraman
Sensors 2022, 22(16), 6190; https://doi.org/10.3390/s22166190 - 18 Aug 2022
Cited by 4 | Viewed by 2709
Abstract
Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this [...] Read more.
Sleep plays a critical role in stroke recovery. However, there are limited practices to measure sleep for individuals with stroke, thus inhibiting our ability to identify and treat poor sleep quality. Wireless, body-worn sensors offer a solution for continuous sleep monitoring. In this study, we explored the feasibility of (1) collecting overnight biophysical data from patients with subacute stroke using a simple sensor system and (2) constructing machine-learned algorithms to detect sleep stages. Ten individuals with stroke in an inpatient rehabilitation hospital wore two wireless sensors during a single night of sleep. Polysomnography served as ground truth to classify different sleep stages. A population model, trained on data from multiple patients and tested on data from a separate patient, performed poorly for this limited sample. Personal models trained on data from one patient and tested on separate data from the same patient demonstrated markedly improved performance over population models and research-grade wearable devices to detect sleep/wake. Ultimately, the heterogeneity of biophysical signals after stroke may present a challenge in building generalizable population models. Personal models offer a provisional method to capture high-resolution sleep metrics from simple wearable sensors by leveraging a single night of polysomnography data. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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Review

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17 pages, 3046 KiB  
Review
Evaluation of the Accuracy of Contactless Consumer Sleep-Tracking Devices Application in Human Experiment: A Systematic Review and Meta-Analysis
by Huifang Zhai, Yonghong Yan, Siqi He, Pinyong Zhao and Bohan Zhang
Sensors 2023, 23(10), 4842; https://doi.org/10.3390/s23104842 - 17 May 2023
Cited by 6 | Viewed by 3328
Abstract
Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review [...] Read more.
Compared with the gold standard, polysomnography (PSG), and silver standard, actigraphy, contactless consumer sleep-tracking devices (CCSTDs) are more advantageous for implementing large-sample and long-period experiments in the field and out of the laboratory due to their low price, convenience, and unobtrusiveness. This review aimed to examine the effectiveness of CCSTDs application in human experiments. A systematic review and meta-analysis (PRISMA) of their performance in monitoring sleep parameters were conducted (PROSPERO: CRD42022342378). PubMed, EMBASE, Cochrane CENTRALE, and Web of Science were searched, and 26 articles were qualified for systematic review, of which 22 provided quantitative data for meta-analysis. The findings show that CCSTDs had a better accuracy in the experimental group of healthy participants who wore mattress-based devices with piezoelectric sensors. CCSTDs’ performance in distinguishing waking from sleeping epochs is as good as that of actigraphy. Moreover, CCSTDs provide data on sleep stages that are not available when actigraphy is used. Therefore, CCSTDs could be an effective alternative tool to PSG and actigraphy in human experiments. Full article
(This article belongs to the Special Issue Sensors in Sleep Monitoring)
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