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.

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.

Investigating the computational power of spiking neurons with non-standard behaviors.

Spiking neural networks have been called the third generation of neural networks. Their main difference with respect to the previous two generations is the use of realistic neuron models. Their computational power has been well studied with respect to threshold gates and sigmoidal neurons. However, biologically realistic models of spiking neurons can produce behaviors that can be computationally relevant, but their power has not been assessed in the same way. This paper studies the computational power of neurons with different behaviors based on the previous analyses conducted by Maass and Schmitt. The studied behaviors are rebound spiking, resonance and bursting. The results of the analysis are presented. A theoretical motivation for this study is presented and a discussion is done on the possible implications of the findings for using networks of spiking neurons for performing computations.