Designing a Smart Pillow for Sleep Quality Remote Monitoring

This paper proposes a low cost IoT solution to detect head movements and positions of a patient by means of Force Sensing Resistors positioned on a pillow and connected to a micro-controller collecting patient data anytime, when sleeping, sending it to the cloud and making it available to healthcare professionals. The impact of this work is focused on monitoring sleep quality, using low-cost and easy to use pillows in an ambulatory scenario, without the need of expensive and dedicated sleeping rooms for sleep monitoring, which most of the times affect patient sleep and degrades the quality of the measurement.In this case it is possible to monitor the patient’s behavior throughout the entire sleep, important for detecting factors causing minor head and neck injuries and even checking for events of long pauses in respiratory rate.

Ubi-SleepNet

Sleep is a fundamental physiological process that is essential for sustaining a healthy body and mind. The gold standard for clinical sleep monitoring is polysomnography(PSG), based on which sleep can be categorized into five stages, including wake/rapid eye movement sleep (REM sleep)/Non-REM sleep 1 (N1)/Non-REM sleep 2 (N2)/Non-REM sleep 3 (N3). However, PSG is expensive, burdensome and not suitable for daily use. For long-term sleep monitoring, ubiquitous sensing may be a solution. Most recently, cardiac and movement sensing has become popular in classifying three-stage sleep, since both modalities can be easily acquired from research-grade or consumer-grade devices (e.g., Apple Watch). However, how best to fuse the data for greatest accuracy remains an open question. In this work, we comprehensively studied deep learning (DL)-based advanced fusion techniques consisting of three fusion strategies alongside three fusion methods for three-stage sleep classification based on two publicly available datasets. Experimental results demonstrate important evidences that three-stage sleep can be reliably classified by fusing cardiac/movement sensing modalities, which may potentially become a practical tool to conduct large-scale sleep stage assessment studies or long-term self-tracking on sleep. To accelerate the progression of sleep research in the ubiquitous/wearable computing community, we made this project open source, and the code can be found at: https://github.com/bzhai/Ubi-SleepNet.

Testing an early online intervention for the treatment of disturbed sleep during the COVID-19 pandemic in self-reported good and poor sleepers (Sleep COVID-19): study protocol for a randomised controlled trial

Background Theoretical models of insomnia suggest that stressful life events, such as the COVID-19 pandemic, can cause acute insomnia (short-term disruptions to sleep). Early interventions may prevent short-term sleep problems from progressing to insomnia disorder. Although cognitive behavioural therapy for insomnia (CBT-I) is effective in treating insomnia disorder, this can be time and resource-intensive. Further, online interventions can be used to deliver treatment to a large number of individuals. The objective of this study is to investigate if an online behavioural intervention, in the form of a leaflet, which has been successfully used alongside CBT-I for acute insomnia, can reduce symptoms of acute insomnia in poor sleepers. Methods A total of 124 self-reported good and poor sleepers will be enrolled in an online stratified randomised controlled trial. After baseline assessments (T1), participants will complete a 1-week pre-intervention sleep monitoring period (T2) where they will complete daily sleep-diaries. Poor sleepers (n = 62) will be randomly allocated to an invention or wait-list group, where they will receive the intervention (T3), or will do so after a 28-day delay. Good sleepers (n = 62) will be randomly assigned to an intervention or no intervention group. All participants will complete a 1-week post intervention sleep monitoring period using daily sleep diaries (T4). Participants will be followed up at 1 week (T5), 1 month (T6) and 3 months (T7) post intervention. The primary outcome measure will be insomnia severity, measured using the Insomnia Severity Index. Secondary outcome measures will include subjective mood and subjective sleep continuity, measured using sleep diaries. Data will be analysed using an intention-to-treat approach. Discussion It is expected that this online intervention will reduce symptoms of acute insomnia in self-reported short-term poor sleepers, and will also prevent the transition to poor sleep in good sleepers. We expect that this will demonstrate the feasibility of online interventions for the treatment and prevention of acute insomnia. Specific advantages of online approaches include the low cost, ease of administration and increased availability of treatment, relative to face-to-face therapy. Trial registration ISRCTN43900695 (Prospectively registered 8th of April 2020).

Automatic Home-based Screening of Obstructive Sleep Apnea using Single Channel Electrocardiogram and SPO2 Signals

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, expensive, and requiring professional sleep technicians. Automatic home-based detection of OSA is welcome and in great demand. It is a fast and effective way for referring OSA suspects to sleep clinics for further monitoring. On-line OSA detection also can be a part of a closed-loop automatic control of the OSA therapeutic/assistive devices. In this paper, several solutions for online OSA detection are introduced and tested on 155 subjects of three different databases. The best combinational solution uses mutual information (MI) analysis for selecting out of ECG and SpO2-based features. Several methods of supervised and unsupervised machine learning are employed to detect apnoeic episodes. To achieve the best performance, the most successful classifiers in four different ternary combination methods are used. The proposed configurations exploit limited use of biological signals, have online working scheme, and exhibit uniform and acceptable performance (over 85%) in all the employed databases. The benefits have not been gathered all together in the previous published methods.

Proposal for a Home Sleep Monitoring Platform Employing a Smart Glove

The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO2) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.

Sleep and COVID-19. A Case Report of a Mild COVID-19 Patient Monitored by Consumer-Targeted Sleep Wearables

Since its first description in Wuhan, China, the novel Coronavirus (SARS-CoV-2) has spread rapidly around the world. The management of this major pandemic requires a close coordination between clinicians, scientists, and public health services in order to detect and promptly treat patients needing intensive care. The development of consumer wearable monitoring devices offers physicians new opportunities for the continuous monitoring of patients at home. This clinical case presents an original description of 55 days of SARS-CoV-2-induced physiological changes in a patient who routinely uses sleep-monitoring devices. We observed that sleep was specifically affected during COVID-19 (Total Sleep time, TST, and Wake after sleep onset, WASO), within a seemingly bidirectional manner. Sleep status prior to infection (e.g., chronic sleep deprivation or sleep disorders) may affect disease progression, and sleep could be considered as a biomarker of interest for monitoring COVID-19 progression. The use of habitual data represents an opportunity to evaluate pathologic states and improve clinical care.

Sleep quality assessment by parameter optimization

Sleep quality measurement is a complex process requires large number of parameters to monitor sleep and sleep cycles. The Gold Standard Polysomnography (PSG) parameters are considered as standard parameters for sleep quality measurement. In the PSG process, number of monitoring parameters are involved for that large number of sensors are used which makes this process complex, expensive and obtrusive. There is need to find optimize parameters which are directly involve in providing accurate information about sleep and reduce the process complexity. Our Parameter Optimization method is based on parameter reduction by finding key parameters and their inter dependent parameters. Sleep monitoring by these optimize parameter is different from both, clinical complex (PSG) used in hospitals and commercially available devices which work on dependent and dynamic parameter sensing. Optimized parameters obtained from PSG parameters are Electrocardiogram (ECG), Electrooculogram (EOG), Electroencephalography (EEG) and Cerebral blood flow (CBF). These key parameters show close correlation with sleep and hence reduce complexity in sleep monitoring by providing simultaneous measurement of appropriate signals for sleep analysis.