Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

High-Performance Flexible Humidity Sensor Based on MoOX Nanoparticle Films for Monitoring Human Respiration and Non-Contact Sensing

Flexible humidity sensors with high sensitivity, fast response time, and outstanding reliability have the potential to revolutionize electronic skin, healthcare, and non-contact sensing. In this study, we employed a straightforward nanocluster deposition technique to fabricate a resistive humidity sensor on a flexible substrate, using molybdenum oxide nanoparticles (MoOx NPs). We systematically evaluated the humidity-sensing behaviors of the MoOx NP film-based sensor and found that it exhibited exceptional sensing capabilities. Specifically, the sensor demonstrated high sensitivity (18.2 near zero humidity), a fast response/recovery time (1.7/2.2 s), and a wide relative humidity (RH) detection range (0-95%). The MoOx NP film, with its closely spaced granular nanostructure and high NP packing density, exhibited insensitivity to mechanical deformation, small hysteresis, good repeatability, and excellent stability. We also observed that the device exhibited distinct sensing kinetics in the range of high and low RH. Specifically, for RH > 43%, the response time showed a linear prolongation with increased RH. This behavior was attributed to two factors: the higher physical adsorption energy of H2O molecules and a multilayer physical adsorption process. In terms of applications, our sensor can be easily attached to a mask and has the potential to monitor human respiration owing to its high sensing performance. Additionally, the sensor was capable of dynamically tracking RH changes surrounding human skin, enabling a non-contact sensing capability. More significantly, we tested an integrated sensor array for its ability to detect moisture distribution in the external environment, demonstrating the potential of our sensor for contactless human-machine interaction. We believe that this innovation is particularly valuable during the COVID-19 epidemic, where cross-infection may be averted by the extensive use of contactless sensing. Overall, our findings demonstrate the tremendous potential of MoOx NP-based humidity sensors for a variety of applications, including healthcare, electronic skin, and non-contact sensing.

Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals.

The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.

Electrothermal sterilization and self-powered real-time respiratory monitoring of reusable mask based on Ag micro-mesh films.

Since the COVID-19 pandemic outbreaks, the utilization of medical masks plays a critical role in reducing the infected risk. However, constructing multifunctional masks to achieve simultaneously self-sterilization, reusability, and respiratory monitoring capability remains still a huge challenge. Herein, a reusable Ag micro-mesh film-based mask is proposed, which enables the capabilities of electrothermal sterilization and self-powered real-time respiratory monitoring. Highly conductive Ag micro-mesh films prepared by continuous draw spinning method demonstrate excellent electrothermal performances for thermal sterilization and serve as working electrode to fabricate triboelectric nanogenerator (TENG) for real-time respiratory monitoring, respectively. Under a low driving voltage of 3.0 V, the surface temperature of Ag micro-mesh film enables a quick increase to over 60 °C within 30 s, which endows thermal sterilization against S. aureus with antibacterial efficiency of 95.58 % within 20 min to achieve the self-sterilization of medical masks. Furthermore, a self-powered alarm system based on the fabricated TENG as respiratory monitor is developed for real-time respiratory monitoring to render a timely treatment for patients in danger of tachypnea and apnea. Consequently, this work has paved a new and practical avenue to achieve reusable multifunctional masks with capabilities of electrothermal sterilization and real-time respiratory monitoring in clinical medicine.

Nasal pressure swings as the measure of inspiratory effort in spontaneously breathing patients with de novo acute respiratory failure.

BACKGROUND: Excessive inspiratory effort could translate into self-inflicted lung injury, thus worsening clinical outcomes of spontaneously breathing patients with acute respiratory failure (ARF). Although esophageal manometry is a reliable method to estimate the magnitude of inspiratory effort, procedural issues significantly limit its use in daily clinical practice. The aim of this study is to describe the correlation between esophageal pressure swings (ΔPes) and nasal (ΔPnos) as a potential measure of inspiratory effort in spontaneously breathing patients with de novo ARF. METHODS: From January 1, 2021, to September 1, 2021, 61 consecutive patients with ARF (83.6% related to COVID-19) admitted to the Respiratory Intensive Care Unit (RICU) of the University Hospital of Modena (Italy) and candidate to escalation of non-invasive respiratory support (NRS) were enrolled. Clinical features and tidal changes in esophageal and nasal pressure were recorded on admission and 24 h after starting NRS. Correlation between ΔPes and ΔPnos served as primary outcome. The effect of ΔPnos measurements on respiratory rate and ΔPes was also assessed. RESULTS: ΔPes and ΔPnos were strongly correlated at admission (R2 = 0.88, p < 0.001) and 24 h apart (R2 = 0.94, p < 0.001). The nasal plug insertion and the mouth closure required for ΔPnos measurement did not result in significant change of respiratory rate and ΔPes. The correlation between measures at 24 h remained significant even after splitting the study population according to the type of NRS (high-flow nasal cannulas [R2 = 0.79, p < 0.001] or non-invasive ventilation [R2 = 0.95, p < 0.001]). CONCLUSIONS: In a cohort of patients with ARF, nasal pressure swings did not alter respiratory mechanics in the short term and were highly correlated with esophageal pressure swings during spontaneous tidal breathing. ΔPnos might warrant further investigation as a measure of inspiratory effort in patients with ARF. TRIAL REGISTRATION: NCT03826797 . Registered October 2016.

Optical Monitoring of Breathing Patterns and Tissue Oxygenation: A Potential Application in COVID-19 Screening and Monitoring.

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.

Combination of wavelet transform and LSTM to detect low functional respiration

Automatic and long-term monitoring of respiratory is in great demand for lung diseases. It gets required greater in these years due to COVID-19 pandemic to reduce medical staff fatigue for checking patient conditions frequently for long time. Kobayashi et al., in our team, developed a device measuring respiratory condition by quantizing the displacement between the 6th and 8th ribs. We introduce long short-term memory (LSTM) neural network to classify patient respiratory signals into the two states of normal and low-functional respirations. The signals were checked by a medical doctor manually for classified into the two states. In the process, they were transformed to frequency-domain spectra with complex-valued wavelet transform, and then quantized the respiratory wavelet spectra due to the large number of spectra patterns. After that, the LSTM learned and classified the processed respiratory signals. The experimental results showed the feasibility to detect the two states. © 2021, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.

Impact of a telemedicine system on work burden and mental health of healthcare providers working with COVID-19: a multicenter pre-post prospective study.

Background: The coronavirus disease 2019 (COVID-19) pandemic impacts not only patients but also healthcare providers. This study seeks to investigate whether a telemedicine system reduces physical contact in addressing the COVID-19 pandemic and mitigates nurses' distress and depression. Methods: Patients hospitalized with COVID-19 in 4 hospitals and 1 designated accommodation measured and uploaded their vital signs to secure cloud storage for remote monitoring. Additionally, a mat-type sensor placed under the bed monitored the patients' respiratory rates. Using the pre-post prospective design, visit counts and health care providers' mental health were assessed before and after the system was introduced. Results: A total of 100 nurses participated in the study. We counted the daily visits for 48 and 69 patients with and without using the telemedicine system. The average patient visits were significantly less with the system (16.3 [5.5-20.3] vs 7.5 [4.5-17.5] times/day, P = .009). Specifically, the visit count for each vital sign assessment was about half with the telemedicine system (all P < .0001). Most nurses responded that the system was easy to use (87.1%), reduced work burden (75.2%), made them feel relieved (74.3%), and was effective in reducing the infection risk in hospitals (79.1%) and nursing accommodations (95.0%). Distress assessed by Impact of Event Scale-Revised and depression by Patient Health Questionnaire-9 were at their minimum even without the system and did not show any significant difference with the system (P = .72 and .57, respectively). Conclusions: Telemedicine-based self-assessment of vital signs reduces nurses' physical contact with COVID-19 patients. Most nurses responded that the system is easy and effective in reducing healthcare providers' infection risk.

What is new in respiratory monitoring?

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony.

Enabling Continuous Wearable Reflectance Pulse Oximetry at the Sternum.

In light of the recent Coronavirus disease (COVID-19) pandemic, peripheral oxygen saturation (SpO2) has shown to be amongst the vital signs most indicative of deterioration in persons with COVID-19. To allow for the continuous monitoring of SpO2, we attempted to demonstrate accurate SpO2 estimation using our custom chest-based wearable patch biosensor, capable of measuring electrocardiogram (ECG) and photoplethysmogram (PPG) signals with high fidelity. Through a breath-hold protocol, we collected physiological data with a wide dynamic range of SpO2 from 20 subjects. The ratio of ratios (R) used in pulse oximetry to estimate SpO2 was robustly extracted from the red and infrared PPG signals during the breath-hold segments using novel feature extraction and PPGgreen-based outlier rejection algorithms. Through subject independent training, we achieved a low root-mean-square error (RMSE) of 2.64 ± 1.14% and a Pearson correlation coefficient (PCC) of 0.89. With subject-specific calibration, we further reduced the RMSE to 2.27 ± 0.76% and increased the PCC to 0.91. In addition, we showed that calibration is more efficiently accomplished by standardizing and focusing on the duration of breath-hold rather than the resulting range in SpO2. The accurate SpO2 estimation provided by our custom biosensor and the algorithms provide research opportunities for a wide range of disease and wellness monitoring applications.

Feasibility assessment of an FBG-based soft sensor embedded into a single-use surgical mask for respiratory monitoring

The use of face masks as respiratory protective equipment (RPE) is considered key to maintaining the quality of life during emergency situations, long-term care and working conditions. Face masks can reduce the virus spread and bacterial infections as well as prevent the inhalation of industrial waste gases. Individuals who wear a facial mask over a prolonged time often reported uncomfortable feelings due to breathing resistance, heat, tightness, and overall discomfort. One of the main indicators used to quantify the level of discomfort induced by RPE is the respiratory rate (RR). In fact, RR can be directly associated with RPE-related unease since the presence of facial masks might intuitively modify the breathing pattern of the users. Unfortunately, still little is known about RR and its variability in response to wearing RPE. In the last year, the massive use of face masks due to COVID-19 pandemic fosters the development of sensors to measure RR once mounted into the medical mask. Among other, fiber Bragg grating sensors (FBGs) have gained growing attention since the intrinsic advantages of small size, lightweight, high metrological properties, and safety. In the present study, a single-use FFP2 surgical mask was instrumented by a soft sensor based on FBG to perform a long-term acquisition (i.e., 20 min) of the respiratory signal during ordinary work activities at the video terminal. The promising results confirmed the high accuracy of the proposed system in the estimation of RR with a maximum discrepancy of -0.69 breaths per minute and mean absolute percentage error of 2.88 % when compared to a reference instrument. Moreover, no saturation of the sensor output occurred during the usage time.

Post-acute care management of a patient with COVID-19 using remote cardiorespiratory monitoring.

Hospital discharge planning can be complex and hospital space is often limited. Patients, including those with COVID-19, can have prolonged symptoms after discharge and often require ongoing monitoring. Furthermore, prolonging hospital stays primarily for monitoring can expose patients to iatrogenic and infectious risks. The patient's overall condition and their home support system factor into the decisions of when and where to discharge patients. Innovations in remote patient monitoring (RPM) now allow for more options in the discharge process. This case report presents a patient with severe COVID-19 pneumonia where RPM was used at discharge to improve home monitoring and clinical follow-up. Additional experience with RPM is necessary to refine its role in post-acute care monitoring.

Validation of telemedicine-based self-assessment of vital signs for patients with COVID-19: A pilot study.

INTRODUCTION: In the ongoing COVID-19 pandemic, the development of a system that would prevent the infection of healthcare providers is in urgent demand. We sought to investigate the feasibility and validity of a telemedicine-based system in which healthcare providers remotely check the vital signs measured by patients with COVID-19. METHODS: Patients hospitalized with confirmed or suspected COVID-19 measured and uploaded their vital signs to secure cloud storage. Additionally, the respiratory rates were monitored using a mat-type sensor placed under the bed. We assessed the time until the values became available on the Cloud and the agreements between the patient-measured vital signs and simultaneous healthcare provider measurements. RESULTS: Between 26 May-23 September 2020, 3835 vital signs were measured and uploaded to the cloud storage by the patients (n=16, median 72 years old, 31% women). All patients successfully learned how to use these devices with a 10-minute lecture. The median time until the measurements were available on the cloud system was only 0.35 min, and 95.2% of the vital signs were available within 5 min of the measurement. The agreement between the patients' and healthcare providers' measurements was excellent for all parameters. Interclass coefficient correlations were as follows: systolic (0.92, p<0.001), diastolic blood pressure (0.86, p<0.001), heart rate (0.89, p<0.001), peripheral oxygen saturation (0.92, p<0.001), body temperature (0.83, p<0.001), and respiratory rates (0.90, p<0.001). CONCLUSIONS: Telemedicine-based self-assessment of vital signs in patients with COVID-19 was feasible and reliable. The system will be a useful alternative to traditional vital sign measurements by healthcare providers during the COVID-19 pandemic.

Non-invasive method to detect high respiratory effort and transpulmonary driving pressures in COVID-19 patients during mechanical ventilation.

BACKGROUND: High respiratory drive in mechanically ventilated patients with spontaneous breathing effort may cause excessive lung stress and strain and muscle loading. Therefore, it is important to have a reliable estimate of respiratory effort to guarantee lung and diaphragm protective mechanical ventilation. Recently, a novel non-invasive method was found to detect excessive dynamic transpulmonary driving pressure (∆PL) and respiratory muscle pressure (Pmus) with reasonable accuracy. During the Coronavirus disease 2019 (COVID-19) pandemic, it was impossible to obtain the gold standard for respiratory effort, esophageal manometry, in every patient. Therefore, we investigated whether this novel non-invasive method could also be applied in COVID-19 patients. METHODS: ∆PL and Pmus were derived from esophageal manometry in COVID-19 patients. In addition, ∆PL and Pmus were computed from the occlusion pressure (∆Pocc) obtained during an expiratory occlusion maneuver. Measured and computed ∆PL and Pmus were compared and discriminative performance for excessive ∆PL and Pmus was assessed. The relation between occlusion pressure and respiratory effort was also assessed. RESULTS: Thirteen patients were included. Patients had a low dynamic lung compliance [24 (20-31) mL/cmH2O], high ∆PL (25 ± 6 cmH2O) and high Pmus (16 ± 7 cmH2O). Low agreement was found between measured and computed ∆PL and Pmus. Excessive ∆PL > 20 cmH2O and Pmus > 15 cmH2O were accurately detected (area under the receiver operating curve (AUROC) 1.00 [95% confidence interval (CI), 1.00-1.00], sensitivity 100% (95% CI, 72-100%) and specificity 100% (95% CI, 16-100%) and AUROC 0.98 (95% CI, 0.90-1.00), sensitivity 100% (95% CI, 54-100%) and specificity 86% (95% CI, 42-100%), respectively). Respiratory effort calculated per minute was highly correlated with ∆Pocc (for esophageal pressure time product per minute (PTPes/min) r2 = 0.73; P = 0.0002 and work of breathing (WOB) r2 = 0.85; P < 0.0001). CONCLUSIONS: ∆PL and Pmus can be computed from an expiratory occlusion maneuver and can predict excessive ∆PL and Pmus in patients with COVID-19 with high accuracy.