Validation of a New Contactless and Continuous Respiratory Rate Monitoring Device Based on Ultra-Wideband Radar Technology.

Respiratory rate (RR) is typically the first vital sign to change when a patient decompensates. Despite this, RR is often monitored infrequently and inaccurately. The Circadia Contactless Breathing Monitor™ (model C100) is a novel device that uses ultra-wideband radar to monitor RR continuously and un-obtrusively. Performance of the Circadia Monitor was assessed by direct comparison to manually scored reference data. Data were collected across a range of clinical and non-clinical settings, considering a broad range of user characteristics and use cases, in a total of 50 subjects. Bland-Altman analysis showed high agreement with the gold standard reference for all study data, and agreement fell within the predefined acceptance criteria of ±5 breaths per minute (BrPM). The 95% limits of agreement were -3.0 to 1.3 BrPM for a nonprobability sample of subjects while awake, -2.3 to 1.7 BrPM for a clinical sample of subjects while asleep, and -1.2 to 0.7 BrPM for a sample of healthy subjects while asleep. Accuracy rate, using an error margin of ±2 BrPM, was found to be 90% or higher. Results demonstrate that the Circadia Monitor can effectively and efficiently be used for accurate spot measurements and continuous bedside monitoring of RR in low acuity settings, such as the nursing home or hospital ward, or for remote patient monitoring.

Basic Motor Skills of Children With Down Syndrome: Creating a Motor Growth Curve.

PURPOSE: To create a motor growth curve based on the Test of Basic Motor Skills for Children with Down Syndrome (BMS) and estimate the age of achieving BMS milestones. METHODS: A multilevel exponential model was applied to create a motor growth curve based on BMS data from 119 children with Down syndrome (DS) aged 2 months to 5 years. Logistic regression was applied to estimate the 50% probability of achieving BMS milestones. RESULTS: The BMS growth curve had the largest increase during infancy with smaller increases as children approached the predicted maximum score. The age at which children with DS have a 50% probability of achieving the milestone sitting was 22 months, for crawling 25 months, and for walking 38 months. CONCLUSIONS: The creation of a BMS growth curve provides a standardization of the gross motor development of children with DS. Physical therapists then may monitor a child's individual progress and improve clinical decisions.

Detecting early signs of deterioration and preventing hospitalizations in skilled nursing facilities using remote respiratory monitoring.

An increase in respiratory rate (RR) can be an early indicator of clinical deterioration, yet it remains an often-neglected vital sign. The most common way of measuring RR is by manually counting chest-wall movements, a time-consuming and error-prone process. Staffing and funding shortages, particularly in post-acute and long-term care, mean these RR measurements are often infrequent, potentially leading to missed diagnoses and preventable readmissions. Here we present a case series from skilled nursing facilities, highlighting how continuous respiratory monitoring using a contactless remote patient monitoring (RPM) system can support clinicians in initiating timely interventions, potentially reducing preventable hospitalizations, mortality, and associated financial implications.

Early detection of deterioration in COVID-19 patients by continuous ward respiratory rate monitoring: a pilot prospective cohort study.

Background: Tachypnea is among the earliest signs of pulmonary decompensation. Contactless continuous respiratory rate monitoring might be useful in isolated COVID-19 patients admitted in wards. We therefore aimed to determine whether continuous monitoring of respiratory patterns in hospitalized patients with COVID-19 predicts subsequent need for increased respiratory support. Methods: Single-center pilot prospective cohort study in COVID-19 patients who were cared for in routine wards. COVID-19 patients who had at least one escalation of pulmonary management were matched to three non-escalated patients. Contactless respiratory monitoring was instituted after patients enrolled, and continued for 15 days unless hospital discharge, initiation of invasive mechanical ventilation, or death occurred. Clinicians were blinded to respiratory rate data from the continuous monitor. The exposures were respiratory features over rolling periods of 30 min, 24 h, and 72 h before respiratory care escalation. The primary outcome was a subsequent escalation in ventilatory support beyond a Venturi mask. Results: Among 125 included patients, 13 exhibited at least one escalation and were each matched to three non-escalated patients. A total of 28 escalation events were matched to 84 non-escalation episodes. The 30-min mean respiratory rate in escalated patients was 23 breaths per minute (bpm) ranging from 13 to 40 bpm, similar to the 22 bpm in non-escalated patients, although with less variability (range 14 to 31 bpm). However, higher respiratory rate variability, especially skewness over 1 day, was associated with higher incidence of escalation events. Our overall model, based on continuous data, had a moderate accuracy with an AUC 0.81 (95%CI: 0.73, 0.88) and a good specificity 0.93 (95%CI: 0.87, 0.99). Conclusion: Our pilot observational study suggests that respiratory rate variability as detected with continuous monitoring is associated with subsequent care escalation during the following 24 h. Continuous respiratory monitoring thus appears to be a valuable increment over intermittent monitoring. Strengths and limitations: Our study was the initial evaluation of Circadia contactless respiratory monitoring in COVID-19 patients who are at special risk of pulmonary deterioration. The major limitation is that the analysis was largely post hoc and thus needs to be confirmed in an out-of-sample population.

Dynamic Microwave Imaging of the Cardiovascular System Using Ultra-Wideband Radar-on-Chip Devices.

OBJECTIVE: Microwave imaging has been investigated for medical applications such as stroke and breast imaging. Current systems typically rely on bench-top equipment to scan at a variety of antenna positions. For dynamic imaging of moving structures, such as the cardiovascular system, much higher imaging speeds are required than what has thus far been reported. Recent innovations in radar-on-chip technology allow for simultaneous high speed data collection at multiple antenna positions at a fraction of the cost of conventional microwave equipment, in a small and potentially portable system. The objective of the current work is to provide proof of concept of dynamic microwave imaging in the body, using radar-on-chip technology. METHODS: Arrays of body-coupled antennas were used with nine simultaneously operated coherent ultra-wideband radar chips. Data were collected from the chest and thigh of a volunteer, with the objective of imaging the femoral artery and beating heart. In addition, data were collected from a phantom to validate system performance. Video data were constructed using beamforming. RESULTS: The location of the femoral artery could successfully be resolved, and a distinct arterial pulse wave was discernable. Cardiac activity was imaged at locations corresponding to the heart, but image quality was insufficient to identify individual anatomical structures. Static and differential imaging of the femur bone proved unsuccessful. CONCLUSION: Using radar chip technology and an imaging approach, cardiovascular activity was detected in the body, demonstrating first steps towards biomedical dynamic microwave imaging. The current portable and modular system design was found unsuitable for static in-body imaging. SIGNIFICANCE: This first proof of concept demonstrates that radar-on-chip could enable cardiovascular imaging in a low-cost, small and portable system. Such a system could make medical imaging more accessible, particularly in ambulatory or long-term monitoring settings.

Performance Evaluation of the Circadia Contactless Breathing Monitor and Sleep Analysis Algorithm for Sleep Stage Classification.

Although polysomnography (PSG) remains the gold standard for studying sleep in the lab, the development of wearable and 'nearable' non-EEG based sleep monitors has the potential to make long-term sleep monitoring in a home environment possible. However, validation of these novel technologies against PSG is required. The current study aims to evaluate the sleep staging performance of the radar-based Circadia Contactless Breathing Monitor (model C100) and proprietary Sleep Analysis Algorithm, both in a home and sleep lab environment, on cohorts of healthy sleepers. The C100 device was initially used to record 17 nights of sleep data from 9 participants alongside PSG, with a subsequent 24 nights of PSG data for validation purposes. Respiration and body movement features were extracted from sensor data, and a machine learning algorithm was developed to perform sleep stage prediction. The algorithm was trained using PSG data obtained in the initial dataset (n=17), and validated using leave- one-subject-out cross-validation. An epoch-by-epoch recall (true positive rate) of 75.0 %, 59.9 %, 74.8 % and 57.1 %, was found for 'Deep', 'Light', 'REM' and 'Wake' respectively. Highly similar results were obtained in the independent validation dataset (n=24), indicating robustness of results and generalizability of the sleep staging model, at least in the healthy population. The device was found to outperform both a consumer and medical grade wrist-worn monitoring device (Fitbit Alta HR and Philips Respironics Actiwatch) on sleep metric estimation accuracy. These results indicate that the developed non-contact monitor forms a viable alternative to existing clinically used wrist-worn methods, and that longitudinal monitoring of sleep stages in a home environment becomes feasible.

UWB Radar for Non-contact Heart Rate Variability Monitoring and Mental State Classification.

Heart rate variability (HRV), as measured by ultra-wideband (UWB) radar, enables contactless monitoring of physiological functioning in the human body. In the current study, we verified the reliability of HRV extraction from radar data, under limited transmitter power. In addition, we conducted a feasibility study of mental state classification from HRV data, measured using radar. Specifically, arctangent demodulation with calibration and low rank approximation have been used for radar signal pre-processing. An adaptive continuous wavelet filter and moving average filter were utilized for HRV extraction. For the mental state classification task, performance of support vector machine, k-nearest neighbors and random forest classifiers have been compared. The developed system has been validated on human participants, with 10 participants for HRV extraction, and three participants for the proof-of-concept mental state classification study. The results of HRV extraction demonstrate the reliability of time-domain parameter extraction from radar data. However, frequency-domain HRV parameters proved to be unreliable under low SNR. The best average overall mental state classification accuracy achieved was 82.34%, which has important implications for the feasibility of mental health monitoring using UWB radar.

Coherent UWB Radar-on-Chip for In-Body Measurement of Cardiovascular Dynamics.

Coherent ultra-wideband (UWB) radar-on-chip technology shows great promise for developing portable and low-cost medical imaging and monitoring devices. Particularly monitoring the mechanical functioning of the cardiovascular system is of interest, due to the ability of radar systems to track sub-mm motion inside the body at a high speed. For imaging applications, UWB radar systems are required, but there are still significant challenges with in-body sensing using low-power microwave equipment and wideband signals. Recently, it was shown for the first time, on a single subject, that the arterial pulse wave can be measured at various locations in the body, using a coherent UWB radar-on-chip technology. This paper provides more substantial evidence, in the form of new measurements and improved methods, to demonstrate that cardiovascular dynamics can be measured using radar-on-chip. Results across four participants were found to be robust and repeatable. Cardiovascular signals were recorded using radar-on-chip systems and electrocardiography (ECG). Through ECG-aligned averaging, the arterial pulse wave could be measured at a number of locations in the body. Pulse arrival time could be determined with high precision, and blood pressure pulse wave propagation through different arteries was demonstrated. In addition, cardiac dynamics were measured from the chest. This paper serves as a first step in developing a portable and low-cost device for long-term monitoring of the cardiovascular system and provides the fundamentals necessary for developing UWB radar-on-chip imaging systems.

Decoding of attentional selection in a cocktail party environment from single-trial EEG is robust to task.

Recently it has been shown to be possible to ascertain the target of a subject's attention in a cocktail party environment from single-trial (~60 s) electroencephalography (EEG) data. Specifically, this was shown in the context of a dichotic listening paradigm where subjects were cued to attend to a story in one ear while ignoring a different story in the other and were required to answer questions on both stories. This paradigm resulted in a high decoding accuracy that correlated with task performance across subjects. Here, we extend this finding by showing that the ability to accurately decode attentional selection in a dichotic speech paradigm is robust to the particular attention task at hand. Subjects attended to one of two dichotically presented stories under four task conditions. These conditions required subjects to 1) answer questions on the content of both stories, 2) detect irregular frequency fluctuations in the voice of the attended speaker 3) answer questions on both stories and detect frequency fluctuations in the attended story, and 4) detect target words in the attended story. All four tasks led to high decoding accuracy (~89%). These results offer new possibilities for creating user-friendly brain computer interfaces (BCIs).