Sleeping for One Week on a Temperature-Controlled Mattress Cover Improves Sleep and Cardiovascular Recovery.

Body temperature should be tightly regulated for optimal sleep. However, various extrinsic and intrinsic factors can alter body temperature during sleep. In a free-living study, we examined how sleep and cardiovascular health metrics were affected by sleeping for one week with (Pod ON) vs. without (Pod OFF), an active temperature-controlled mattress cover (the Eight Sleep Pod). A total of 54 subjects wore a home sleep test device (HST) for eight nights: four nights each with Pod ON and OFF (>300 total HST nights). Nightly sleeping heart rate (HR) and heart rate variability (HRV) were collected. Compared to Pod OFF, men and women sleeping at cooler temperatures in the first half of the night significantly improved deep (+14 min; +22% mean change; p = 0.003) and REM (+9 min; +25% mean change; p = 0.033) sleep, respectively. Men sleeping at warm temperatures in the second half of the night significantly improved light sleep (+23 min; +19% mean change; p = 0.023). Overall, sleeping HR (-2% mean change) and HRV (+7% mean change) significantly improved with Pod ON (p < 0.01). To our knowledge, this is the first study to show a continuously temperature-regulated bed surface can (1) significantly modify time spent in specific sleep stages in certain parts of the night, and (2) enhance cardiovascular recovery during sleep.

SleepPPG-Net: A Deep Learning Algorithm for Robust Sleep Staging From Continuous Photoplethysmography.

Sleep staging is an essential component in the diagnosis of sleep disorders and management of sleep health. Sleep is traditionally measured in a clinical setting and requires a labor-intensive labeling process. We hypothesize that it is possible to perform automated robust 4-class sleep staging using the raw photoplethysmography (PPG) time series and modern advances in deep learning (DL). We used two publicly available sleep databases that included raw PPG recordings, totalling 2,374 patients and 23,055 hours of continuous data. We developed SleepPPG-Net, a DL model for 4-class sleep staging from the raw PPG time series. SleepPPG-Net was trained end-to-end and consists of a residual convolutional network for automatic feature extraction and a temporal convolutional network to capture long-range contextual information. We benchmarked the performance of SleepPPG-Net against models based on the best-reported state-of-the-art (SOTA) algorithms. When benchmarked on a held-out test set, SleepPPG-Net obtained a median Cohen's Kappa ( κ) score of 0.75 against 0.69 for the best SOTA approach. SleepPPG-Net showed good generalization performance to an external database, obtaining a κ score of 0.74 after transfer learning. Overall, SleepPPG-Net provides new SOTA performance. In addition, performance is high enough to open the path to the development of wearables that meet the requirements for usage in clinical applications such as the diagnosis and monitoring of obstructive sleep apnea.

Detecting beats in the photoplethysmogram: benchmarking open-source algorithms.

The photoplethysmogram (PPG) signal is widely used in pulse oximeters and smartwatches. A fundamental step in analysing the PPG is the detection of heartbeats. Several PPG beat detection algorithms have been proposed, although it is not clear which performs best.Objective:This study aimed to: (i) develop a framework with which to design and test PPG beat detectors; (ii) assess the performance of PPG beat detectors in different use cases; and (iii) investigate how their performance is affected by patient demographics and physiology.Approach:Fifteen beat detectors were assessed against electrocardiogram-derived heartbeats using data from eight datasets. Performance was assessed using theF1score, which combines sensitivity and positive predictive value.Main results:Eight beat detectors performed well in the absence of movement withF1scores of ≥90% on hospital data and wearable data collected at rest. Their performance was poorer during exercise withF1scores of 55%-91%; poorer in neonates than adults withF1scores of 84%-96% in neonates compared to 98%-99% in adults; and poorer in atrial fibrillation (AF) withF1scores of 92%-97% in AF compared to 99%-100% in normal sinus rhythm.Significance:Two PPG beat detectors denoted 'MSPTD' and 'qppg' performed best, with complementary performance characteristics. This evidence can be used to inform the choice of PPG beat detector algorithm. The algorithms, datasets, and assessment framework are freely available.

Remote health diagnosis and monitoring in the time of COVID-19.

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases, with a large spectrum of symptoms. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID-19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g. hotels). In particular, facilitating contact tracing with proximity and location tracing apps was adopted in many countries very rapidly. Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper, we present a review of remote health monitoring initiatives taken in 20 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.