Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

Background: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals.

Objective: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers.

Occurrence of Relative Bradycardia and Relative Tachycardia in Individuals Diagnosed With COVID-19.

The COVID-19 disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become one of the worst global pandemics of the century. Wearable devices are well suited for continuously measuring heart rate. Here we show that the Resting Heart Rate is modified for several weeks following a COVID-19 infection.

Measurement of respiratory rate using wearable devices and applications to COVID-19 detection.

We show that heart rate enabled wearable devices can be used to measure respiratory rate. Respiration modulates the heart rate creating excess power in the heart rate variability at a frequency equal to the respiratory rate, a phenomenon known as respiratory sinus arrhythmia. We isolate this component from the power spectral density of the heart beat interval time series, and show that the respiratory rate thus estimated is in good agreement with a validation dataset acquired from sleep studies (root mean squared error = 0.

Assessment of physiological signs associated with COVID-19 measured using wearable devices.

Respiration rate, heart rate, and heart rate variability (HRV) are some health metrics that are easily measured by consumer devices, which can potentially provide early signs of illness. Furthermore, mobile applications that accompany wearable devices can be used to collect relevant self-reported symptoms and demographic data. This makes consumer devices a valuable tool in the fight against the COVID-19 pandemic.

Development and validation of a novel non-contact monitor of nocturnal respiration for identifying sleep-disordered breathing in patients with heart failure.

Aims: At least 50% of patients with heart failure (HF) may have sleep-disordered breathing (SDB). Overnight in-hospital polysomnography (PSG) is considered the gold standard for diagnosis, but a lack of access to such testing contributes to under-diagnosis of SDB. Therefore, there is a need for simple and reliable validated methods to aid diagnosis in patients with HF.

Wireless sleep measurement: sensing sleep and breathing patterns using radio-frequency sensors.

Despite the fact that we spend nearly one third of our lives asleep, surprisingly little was known about sleep until the 20th century. Now, sleep medicine is firmly established as a significant branch of medical practice, taking its roots strongly from the work of Nathaniel Kleitman and colleagues at the University of Chicago in the 1950s. They were the first to show the existence of rapid eye movement (REM) sleep?commonly associated with ?dreaming?and began the process of opening our eyes to the complex physiological processes that occur during sleep.

A pilot study of the nocturnal respiration rates in COPD patients in the home environment using a non-contact biomotion sensor.

Nocturnal respiration rate parameters were collected from 20 COPD subjects over an 8 week period, to determine if changes in respiration rate were associated with exacerbations of COPD. These subjects were primarily GOLD Class 2 to 4, and had been recently discharged from hospital following a recent exacerbation. The respiration rates were collected using a non-contact radio-frequency biomotion sensor which senses respiratory effort and body movement using a short-range radio-frequency sensor.

A comparison of radio-frequency biomotion sensors and actigraphy versus polysomnography for the assessment of sleep in normal subjects.

Purpose: This paper aims to compare the absolute performance of three noncontact sleep measurement devices for measuring sleep parameters in normal subjects against polysomnography and to assess their relative performance.

Methods: The devices investigated were two noncontact radio-frequency biomotion sensors (SleepMinder (SM) and SleepDesign (HSL-101)) and an actigraphy-based system (Actiwatch). Overnight polysomnography measurements were carried out in 20 normal subjects, with simultaneous assessment of sleep parameters using the three devices.

Comparison of a novel non-contact biomotion sensor with wrist actigraphy in estimating sleep quality in patients with obstructive sleep apnoea.

Ambulatory monitoring is of major clinical interest in the diagnosis of obstructive sleep apnoea syndrome. We compared a novel non-contact biomotion sensor, which provides an estimate of both sleep time and sleep-disordered breathing, with wrist actigraphy in the assessment of total sleep time in adult humans suspected of obstructive sleep apnoea syndrome. Both systems were simultaneously evaluated against polysomnography in 103 patients undergoing assessment for obstructive sleep apnoea syndrome in a hospital-based sleep laboratory (84 male, aged 55 ± 14 years and apnoea-hypopnoea index 21 ± 23).

Assessment of sleep-disordered breathing using a non-contact bio-motion sensor.

Obstructive sleep apnoea is a highly prevalent but under-diagnosed disorder. The gold standard for diagnosis of obstructive sleep apnoea is inpatient polysomnography. This is resource intensive and inconvenient for the patient, and the development of ambulatory diagnostic modalities has been identified as a key research priority.

Real time breathing rate estimation from a non contact biosensor.

An automated real time method for detecting human breathing rate from a non contact biosensor is considered in this paper. The method has low computational and RAM requirements making it well-suited to real-time, low power implementation on a microcontroller. Time and frequency domain methods are used to separate a 15s block of data into movement, breathing or absent states; a breathing rate estimate is then calculated.

Sleep/wake measurement using a non-contact biomotion sensor.

We studied a novel non-contact biomotion sensor, which has been developed for identifying sleep/wake patterns in adult humans. The biomotion sensor uses ultra low-power reflected radiofrequency waves to determine the movement of a subject during sleep. An automated classification algorithm has been developed to recognize sleep/wake states on a 30-s epoch basis based on the measured movement signal.

Physiotherapy for sleep disturbance in chronic low back pain: a feasibility randomised controlled trial.

Background: Sleep disturbance is becoming increasingly recognised as a clinically important symptom in people with chronic low back pain (CLBP, low back pain >12 weeks), associated with physical inactivity and depression. Current research and international clinical guidelines recommend people with CLBP assume a physically active role in their recovery to prevent chronicity, but the high prevalence of sleep disturbance in this population may be unknowingly limiting their ability to participate in exercise-based rehabilitation programmes and contributing to poor outcomes. There is currently no knowledge concerning the effectiveness of physiotherapy on sleep disturbance in people with chronic low back pain and no evidence of the feasibility of conducting randomized controlled trials that comprehensively evaluate sleep as an outcome measure in this population.

Towards using photo-plethysmogram amplitude to measure blood pressure during sleep.

There is considerable interest in non-intrusive and reliable continuous ambulatory blood pressure measurement systems. Pulse amplitude is the peak to trough amplitude of the photo-plethysmogram signal. We compared pulse amplitude with a currently popular parameter, the pulse arrival time (PAT), for estimating continuous systolic blood pressure (SBP).

SleepMinder: an innovative contact-free device for the estimation of the apnoea-hypopnoea index.

We describe an innovative sensor technology (SleepMinder) for contact-less and convenient measurement of sleep and breathing in the home. The system is based on a novel non-contact biomotion sensor and proprietary automated analysis software. The biomotion sensor uses an ultra low-power radio-frequency transceiver to sense the movement and respiration of a subject.

Objective and subjective assessment of sleep in chronic low back pain patients compared with healthy age and gender matched controls: a pilot study.

Background: While approximately 70% of chronic low back pain (CLBP) sufferers complain of sleep disturbance, current literature is based on self report measures which can be prone to bias and no objective data of sleep quality, based exclusively on CLBP are available. In accordance with the recommendations of The American Sleep Academy, when measuring sleep, both subjective and objective assessments should be considered as the two are only modestly correlated, suggesting that each modality assesses different aspects of an individual's sleep experience. Therefore, the purpose of this study was to expand previous research into sleep disturbance in CLBP by comparing objective and subjective sleep quality in participants with CLBP and healthy age and gender matched controls, to identify correlates of poor sleep and to test logistics and gather information prior to a larger study.

Assessment of sleep/wake patterns using a non-contact biomotion sensor.

We evaluate a contact-less continuous measuring system measuring respiration and activity patterns system for identifying sleep/wake patterns in adult humans. The system is based on the use of a novel non-contact biomotion sensor, and an automated signal analysis and classification system. The sleep/wake detection algorithm combines information from respiratory frequency, magnitude, and movement to assign 30 s epochs to either wake or sleep.

Multimodal detection of sleep apnoea using electrocardiogram and oximetry signals.

A method for the detection of sleep apnoea, suitable for use in the home environment, is presented. The method automatically analyses night-time electrocardiogram (ECG) and oximetry recordings and identifies periods of normal and sleep-disordered breathing (SDB). The SDB is classified into one of six classes: obstructive, mixed and central apnoeas, and obstructive, mixed and central hypopnoeas.

A portable automated assessment tool for sleep apnea using a combined Holter-oximeter.

Study Objectives: Resource limitations have raised interest in portable monitoring systems that can be used by specialist sleep physicians as part of an overall strategy to improve access to the diagnosis of sleep apnea. This study validates a combined electrocardiogram and oximetry recorder (Holter-oximeter) against simultaneous polysomnography for detection of sleep apnea.

Design: Prospective study.

Combined electrocardiogram and photoplethysmogram measurements as an indicator of objective sleepiness.

There is considerable interest in unobtrusive and portable methods of monitoring sleepiness outside the laboratory setting. This study evaluates the usefulness of combined electrocardiogram (ECG) and photoplethysmogram (PPG) measurements for estimating psychomotor vigilance. The psychomotor vigilance test (PVT) was performed at various points over the course of a day, and one channel each of ECG and PPG was recorded simultaneously.

Electrocardiogram recording as a screening tool for sleep disordered breathing.

Study Objectives: Transient changes in heart rate associated with obstructive apneas have been suggested for screening of sleep disordered breathing (SDB). This study prospectively compares the outcomes of an automated ECG-based SDB screening tool with simultaneous polysomnography.

Methods: The previously-developed automated algorithm was applied to a single channel ECG obtained during standard overnight polysomnography (92 subjects) to obtain an apnea-hypopnea index (AHI) estimate.

An evaluation of a non-contact biomotion sensor with actimetry.

Actimetry is a widely accepted technology for the diagnosis and monitoring of sleep disorders such as insomnia, circadian sleep/wake disturbance, and periodic leg movement. In this study we investigate a very sensitive non-contact biomotion sensor to measure actimetry and compare its performance to wrist-actimetry. A data corpus consisting of twenty subjects (ten normals, ten with sleep disorders) was collected in the unconstrained home environment with simultaneous non-contact sensor and ActiWatch actimetry recordings.

Automated detection of paroxysmal atrial fibrillation from inter-heartbeat intervals.

An automated method for detecting episodes of probable paroxysmal atrial fibrillation based on processing blocks of inter-heartbeat intervals is considered. The method has very low computational requirements making it well-suited to near real-time, low power applications. A supervised linear discriminant classifier is used to estimate the likelihood of a block of inter-heartbeat intervals containing paroxysmal atrial fibrillation (PAF).

Comparison of baroreflex sensitivity measures for assessing subjects with obstructive sleep apnea.

Baroreflex sensitivity (BRS) is assessed in subjects with and without obstructive sleep apnea (OSA) using the time domain direct sequence technique, the spectral transfer function (TF) technique and the alpha index technique. All three measures showed a significantly depressed BRS value in subjects with severe apnea. The high frequency (HF) component of the spectral measures showed higher correlation with the sequence technique measures than the low frequency (LF) component.