Sleep evaluation by a newly developed PVDF sensor non-contact sheet: a comparison with standard polysomnography and wrist actigraphy

A Soft Bioelectronic Patch for Simultaneous Respiratory and Cardiovascular Monitoring

Sleep is critical to maintaining physical and mental health. Measuring physiological parameters to quantify sleep quality without uncomfortable user experience remains highly desired but a challenge. Here, this work develops a soft bioelectronic patch to perform simultaneous respiration and cardiovascular monitoring during sleep in a wearable and non-invasive manner. The soft bioelectronic patch system is mainly composed of a pressure sensor, a flexible printed circuit for signal processing, and a soft thermoplastic urethane mold for assembling different functional modules. The soft bioelectronic patch holds a sensitivity of >0.12 V kPa-1 and a remarkable low-frequency response from 0.5 to 15 Hz. It is demonstrated to continuously monitor respiration and heartbeat during the whole night, which could be harnessed for sleep monitoring and obstructive sleep apnea-hypopnea syndrome diagnosis. The reported soft bioelectronic patch represents a simple and convenient platform technology for sleep study.

Sleep in the Athlete

Sleep is important for not only general health but also for lowering injury risk and maintaining athletic performance. Sleep disorders are prevalent in athletes, and taking a sleep history, evaluating sleep quality, and addressing other related factors including mental health are essential in diagnosing and understanding sleep disorders. Other methods such as polysomnography, actigraphy, and sheet sensors can also be used. Treatment options for sleep disorders include sleep hygiene, cognitive behavioral therapy, medication, and addressing contributing factors. For athletes, sleep can also be affected by factors such as travel fatigue and jet lag, which should be taken into consideration.

Unobstructive Heartbeat Monitoring of Sleeping Infants and Young Children Using Sheet-Type PVDF Sensors

Techniques for noninvasively acquiring the vital information of infants and young children are considered very useful in the fields of healthcare and medical care. An unobstructive measurement method for sleeping infants and young children under the age of 6 years using a sheet-type vital sensor with a polyvinylidene fluoride (PVDF) pressure-sensitive layer is demonstrated. The signal filter conditions to obtain the ballistocardiogram (BCG) and phonocardiogram (PCG) are discussed from the waveform data of infants and young children. The difference in signal processing conditions was caused by the physique of the infants and young children. The peak-to-peak interval (PPI) extracted from the BCG or PCG during sleep showed an extremely high correlation with the R-to-R interval (RRI) extracted from the electrocardiogram (ECG). The vital changes until awakening in infants monitored using a sheet sensor were also investigated. In infants under one year of age that awakened spontaneously, the distinctive vital changes during awakening were observed. Understanding the changes in the heartbeat and respiration signs of infants and young children during sleep is essential for improving the accuracy of abnormality detection by unobstructive sensors.

Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Distinguishing sleep from wake with a radar sensor: a contact-free real-time sleep monitor

This work aimed to evaluate whether a radar sensor can distinguish sleep from wakefulness in real time. The sensor detects body movements without direct physical contact with the subject and can be embedded in the roof of a hospital room for completely unobtrusive monitoring. We conducted simultaneous recordings with polysomnography, actigraphy, and radar on two groups: healthy young adults (n = 12, four nights per participant) and patients referred to a sleep examination (n = 28, one night per participant). We developed models for sleep/wake classification based on principles commonly used by actigraphy, including real-time models, and tested them on both datasets. We estimated a set of commonly reported sleep parameters from these data, including total-sleep-time, sleep-onset-latency, sleep-efficiency, and wake-after-sleep-onset, and evaluated the inter-method reliability of these estimates. Classification results were on-par with, or exceeding, those often seen for actigraphy. For real-time models in healthy young adults, accuracies were above 92%, sensitivities above 95%, specificities above 83%, and all Cohen's kappa values were above 0.81 compared to polysomnography. For patients referred to a sleep examination, accuracies were above 81%, sensitivities about 89%, specificities above 53%, and Cohen's kappa values above 0.44. Sleep variable estimates showed no significant intermethod bias, but the limits of agreement were quite wide for the group of patients referred to a sleep examination. Our results indicate that the radar has the potential to offer the benefits of contact-free real-time monitoring of sleep, both for in-patients and for ambulatory home monitoring.

Detection of Low Cardiac Index using a Polyvinylidene Fluoride-Based Wearable Ring and Convolutional Neural Networks

This study investigated the use of a wearable ring made of polyvinylidene fluoride film to identify a low cardiac index (≤2 L/min). The waveform generated by the ring contains patterns that may be indicative of low blood pressure and/or high vascular resistance, both of which are markers of a low cardiac index. In particular, the waveform contains reflection waves whose timing and amplitude are correlated with pulse travel time and vascular resistance, respectively. Hence, the pattern of the waveform is expected to vary in response to changes in blood pressure and vascular resistance. By analyzing the morphology of the waveform, our aim was to create a tool to identify patients with low cardiac index. This was done using a convolutional neural network which was trained on data from animal models. The model was then tested on waveforms that were collected from patients undergoing pulmonary artery catheterization. The results indicate high accuracy in classifying patients with a low cardiac index, achieving an area under the receiver operating characteristics and precision-recall curves of 0.88 and 0.71, respectively.

Single-layered ultra-soft washable smart textiles for all-around ballistocardiograph, respiration, and posture monitoring during sleep

Good sleep is considered to be the cornerstone for maintaining both physical and mental health. However, nearly one billion people worldwide suffer from various sleep disorders. To date, polysomnography (PSG) is the most commonly used sleep-monitoring technology,however, it is complex, intrusive, expensive and uncomfortable. Unfortunately, present noninvasive monitoring technologies cannot simultaneously achieve high sensitivity, multi-parameter monitoring and comfort. Here, we present a single-layered, ultra-soft, smart textile for all-around physiological parameters monitoring and healthcare during sleep. With a high-pressure sensitivity of 10.79 mV/Pa, a wide working frequency bandwidth from 0 Hz to 40 Hz, good stability, and decent washability, the single-layered ultra-soft smart textile is simultaneously capable of real-time detection and tracking of dynamic changes in sleep posture, and subtle respiration and ballistocardiograph (BCG) monitoring. Using the set of patient generated health data, an obstructive sleep apnea-hypopnea syndrome (OSAHS) monitoring and intervention system was also developed to improve the sleep quality and prevent sudden death during sleep. This work is expected to pave a new and practical pathway for physiological monitoring during sleep.

Sleep quality, sleep duration, and their association with hypertension prevalence among low-income oldest-old in a rural area of China: A population-based study

OBJECTIVE

The relationship among sleep quality, sleep duration and hypertension prevalence is controversial in different age groups and genders. This study aimed to investigate sleep quality, sleep duration and their association with hypertension prevalence among low-income oldest-old in a rural area of China.

METHODS

A cross-sectional survey was conducted in a representative sample of 1066 adults aged 80-99 years in 2017. Logistic regression analysis was performed.

RESULTS

Among males, sleep durations of <6 h and 6-<7 h were significantly associated with hypertension prevalence, with odds ratios (ORs) of 3.15 (95% confidence interval (CI) 1.37 to 7.23) and 2.38 (95% CI 1.22 to 4.63), respectively. Among females, only the sleep duration of <6 h was associated with increased OR of hypertension of 3.49 (95% CI 1.50 to 8.09). Poor sleep quality was associated with hypertension for both genders (OR_men 1.67, 95% CI 1.12 to 2.49; OR_women 1.91, 95% CI 1.29 to 2.82). For women, a combination of poor sleep quality and any group of sleep duration, except for 7-<8 h, was associated with higher hypertension prevalence. For men, only the combination of poor sleep quality and short sleep duration (<7 h) was associated with high hypertension prevalence.

CONCLUSION

Short sleep duration and poor sleep quality are associated with hypertension prevalence of oldest-old. The prevention of hypertension in older adults should be investigated from the perspective of sleep improvement.