Respiratory Rate Monitoring Research Articles

An ultrasensitive multimodal intracranial pressure biotelemetric system enabled by exceptional point and iontronics.

The accurate monitoring of vital physiological parameters, exemplified by heart rate, respiratory rate, and intracranial pressure (ICP), is of paramount importance, particularly for managing severe cranial injuries. Despite the rapid development of implantable ICP sensing systems over the past decades, they still suffer from, for example, wire connection, low sensitivity, poor resolution, and the inability to monitor multiple variables simultaneously. Here, we propose an ultrasensitive multimodal biotelemetric system that amalgamates an iontronic pressure transducer with exceptional point (EP) operation for the monitoring of ICP signals. The proposed system can exhibit extraordinary performance regarding the detection of minuscule ICP fluctuation, demonstrated by the sensitivity of 115.95 kHz/mmHg and the sensing resolution down to 0.003 mmHg. Our system excels not only in the accurate quantification of ICP levels but also in distinguishing respiration and cardiac activities from ICP signals, thereby achieving the multimodal monitoring of ICP, respiratory, and heart rates within a single system. Our work may provide a pragmatic avenue for the real-time wireless monitoring of ICP and thus hold great potential to be extended to the monitoring of other vital physiological indicators.

A 3-D-Printed Multiparametric Wearable System Based on Fiber Bragg Gratings for Shoulder Movement and Respiratory Rate Monitoring

A 3-D-Printed Multiparametric Wearable System Based on Fiber Bragg Gratings for Shoulder Movement and Respiratory Rate Monitoring

Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring

Successful implementation of remote monitoring of vital signs outside of the hospital setting hinges on addressing three crucial unmet needs: longer-term wear, skin comfort and signal quality. Earlier, we developed a Health Patch research platform that uses self-adhesive dry electrodes to measure vital digital biomarkers. Here, we report on the analytical validation for heart rate, heart rate variability and respiration rate. Study design included n = 25 adult participants with data acquisition during a 30-minute exercise protocol involving rest, squats, slow, and fast cycling. The Shimmer3 ECG Unit and Cosmed K5, were reference devices. Data analysis showed good agreement in heart rate and marginal agreement in respiratory rate, with lower agreement towards higher respiratory rates. The Lin’s concordance coefficient was 0.98 for heart rate and 0.56 for respiratory rate. Heart rate variability (RMSSD) had a coefficient of 0.85. Participants generally expressed a positive experience with the technology, with some minor irritation from the medical adhesive. The results highlighted potential of this technology for short-to-medium term clinical use for cardiorespiratory health, due to its reliability, accuracy, and compact design. Such technology could become instrumental for remote monitoring providing healthcare professionals with continuous data, remote assessment and enhancing patient outcomes in cardiorespiratory health management.

Speckle Vibrometry for Contactless Instantaneous Heart Rate and Respiration Rate Monitoring on Mechanically Ventilated Patients.

Objective: Contactless monitoring of instantaneous heart rate and respiration rate has a significant clinical relevance. This work aims to use Speckle Vibrometry (i.e., based on the secondary laser speckle effect) to contactlessly measure these two vital signs in an intensive care unit. Methods: In this work, we propose an algorithm for the estimation of instantaneous heart rate and respiration rate from mechanically ventilated patients. The algorithm uses multiple regions, principal component analysis, and dominant angle analysis. A semi-automated peak detection method is implemented to precisely label the aortic valve opening peak within the cardiac waveform. Results: Compared with electrocardiography, the present work achieves limits of agreement of [-2.19, 1.73] beats per minute of instantaneous heart rate. The measurement spot is on the chest covered with two to three layers of duvet blankets. Compared with the airway flow signal measured by the mechanical ventilator, the present work achieves limits of agreement of [-0.68, 0.46] respirations per minute of instantaneous respiration rate. Conclusions: These results showcased Speckle Vibrometry's potential in vital sign monitoring in a clinical setting. Significance: This is the first human clinical study for Speckle Vibrometry.

Respiratory rate monitoring based on all-fiber strain-induced humidity sensor

In this paper, an agarose coated-fiber Bragg grating (AG-FBG) based respiratory rate (RR) sensor for evaluating respiratory function and health status by all-fiber strain-induced humidity-sensitive material is proposed and experimentally demonstrated. The variation of the environment humidity leads to changes in the film morphology of AG, i.e., expansion and contraction, which impose strains on FBG to dynamically shift the central wavelength. In the experiment, the average sensitivity of the proposed AG-FBG-based RR sensor is 23.6 pm/%RH in the range of 30 %RH–90 %RH. Under the monitoring of a commercial FBG demodulator with a wavelength resolution of 1 pm, rich human breathing information is collected. The RR monitoring under different human postures and breathing patterns are respectively measured. In one breathing cycle, the response time and recovery time are 778 ms and 762 ms, respectively. The proposed sensor is simple and low-cost, shows easy signal demodulation, stable signal wavelength variation, and high repeatability, which has potential applications in vital signs monitoring and medical treatment.

A novel computational signal processing framework towards multimodal vital signs extraction using neck-worn wearable devices

Pulse rate (PR) and respiratory rate (RR) are two of the most important vital signs. Monitoring them would benefit from easy-to-use technologies. Hence, wearable devices would, in principle, be ideal candidates for such systems. The neck, although highly susceptible to artifacts, presents an attractive location for a diverse pool of physiological biomarkers monitoring purposes such as airflow sensing in a non-obstructive manner. This paper presents a methodology for PR and RR estimation using photoplethysmography (PPG) and accelerometry (Acc) sensors placed on the neck. Neck PPG and Acc signals were recorded from 22 healthy participants for RR estimation, where the resting subjects performed guided breathing following a visual metronome. Neck PPG signals were obtained from 16 healthy participants who breathed through an altitude generator machine in order to acquire a wider range of PR readings while at rest. The proposed methodology was able to provide rate estimates via a combination of recursive FFT-based dominance scoring coupled with an exponentially weighted moving average (EWMA)-driven aggregation scheme. The recursion aimed at bypassing sudden intra-window amplitude deviations caused by momentary artifacts, while the EWMA-based aggregation was utilized for handling inter-window artifact-induced deviations. To further improve estimation stability and confidence, estimates were calculated in the form of rate bands taking into account the relevant clinically acceptable error margins, and results when considering rate values and rate bands are presented and discussed. The framework was able to achieve an overall pulse rate value accuracy of 93.67±7.64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.67\\pm 7.64$$\\end{document}% within the clinically acceptable ± 5 BPM with reference to the gold-standard reference devices while providing an overall respiratory rate value accuracy within the clinically appropriate ± 3 BrPM of 94.94±3.56\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$94.94\\pm 3.56$$\\end{document}% with reference to the guiding visual metronome, and 88.4±7.63\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.4\\pm 7.63$$\\end{document}% with respect to the gold-standard reference device. The proposed methodology achieves acceptable PR and RR estimation capabilities, even when signals are acquired from an unusual location such as the neck. This work introduces novel ideas that can lead to the development of medical device outputs for PR and RR monitoring, especially capitalizing on the advantages of the neck as a multi-modal physiological monitoring location.

Development of a novel humidity sensor for breath pattern recognition using large dislocation hollow core fiber infused with nano-carbon quantum water gel microfiber

This study presents a humidity sensor based on a large-core-offset hollow core optical fiber (LD-HCF) combined with polyvinyl alcohol carbon quantum dots (PVA-CQDs) nanocomposite microfiber for monitoring human respiratory. PVA-CQDs was introduced into the hollow core structure of LD-HCF to develope the respiratory sensor. The sensor has been integrated into a respiratory monitoring mask, exposed to ultraviolet (UV) light and demonstrated with a humidity sensitivity of 0.1721 nm/%RH as well as response and recovery times of 0.232 s and 0.252 s, respectively. The mask provides the real-time monitoring of human respiratory rates during various activities, including walking, standing, coughing, running, apnea 1 time and apnea 3 times. The typical respiratory patterns have been classified with a remarkable accuracy of 97.62 % using a WOA-1D CNN-LSTM neural network. The respiratory monitoring device is proposed by optical fiber, offering a promising potential in the real-time monitoring for healthcare process.

Temperature self-compensated dual core fiber-optic sensor integrated with moisture sensitive MIL-96(Al) film for breath detection

In this paper, a dual-core fiber optic sensor has been proposed for dynamic monitoring of temperature and humidity. The side core is polished into a D-type optical fiber, and a layer of nano-silver film and a layer of polydimethylsiloxane (PDMS) are deposited to construct a surface plasmon resonance (SPR) temperature compensator. The middle core is constructed as the long-period fiber grating (LPFG) sensing channel. The mode conversion of LPFG is achieved by TiO2 film, and its refractive index (RI) sensitivity is increased by about 4 times. Meanwhile, a layer of moisture sensitive MIL-96(Al) film is grown outside the TiO2 film. Due to the particular pore structure for water molecules, it is possible to change the effective RI of MIL-96(Al) film by absorbing water molecules. Research has shown that the LPFG sensor can achieve a relative humidity sensitivity of −0.411 nm/%RH. Finally, the sensor successfully achieved real-time dynamic monitoring of respiratory rate.

Towards the Instrumentation of Facemasks Used as Personal Protective Equipment for Unobtrusive Breathing Monitoring of Workers.

This study focuses on the integration and validation of a filtering face piece 3 (FFP3) facemask module for monitoring breathing activity in industrial environments. The key objective is to ensure accurate, real-time respiratory rate (RR) monitoring while maintaining workers' comfort. RR monitoring is conducted through temperature variations detected using temperature sensors tested in two configurations: sensor t1, integrated inside the exhalation valve and necessitating structural mask modifications, and sensor t2, mounted externally in a 3D-printed structure, thus preserving its certification as a piece of personal protective equipment (PPE). Ten healthy volunteers participated in static and dynamic tests, simulating typical daily life and industrial occupational activities while wearing the breathing activity monitoring module and a chest strap as a reference instrument. These tests were carried out in both indoor and outdoor settings. The results demonstrate comparable mean absolute error (MAE) for t1 and t2 in both indoor (i.e., 0.31 bpm and 0.34 bpm) and outdoor conditions (i.e., 0.43 bpm and 0.83 bpm). During simulated working activities, both sensors showed consistency with MAE values in static tests and were not influenced by motion artifacts, with more than 97% of RR estimated errors within ±2 bpm. These findings demonstrate the effectiveness of integrating a smart module into protective masks, enhancing occupational health monitoring by providing continuous and precise RR data without requiring additional wearable devices.

THE INFLUENCE OF EVENING TWILIGHT ON THE ATTENTION OF DRIVERS OF DIFFERENT AGES IN CITY CONDITIONS

The article examines the issue of driver safety in cities in the evening twilight. A group of drivers aged 20 to 40 years with a total driving experience of 3 to 16 years was studied. Drivers with 3-5 years of driving experience had an average age of 25 years, and drivers with 10-16 years of driving experience had an average age of 34 years. Drivers drove along known routes at dusk and after dark. We measured drivers’ attention with a Pupil Labs headset, as well as functional state shifts with a respiratory rate monitor. The "Neulog Respiration Monitor Belt" device was used to determine the parameters of external breathing of drivers. Previously, we measured breathing parameters when the driver was in a calm state. Since the morning, the drivers’ breathing rate has been 10-12 breathing cycles. The results of the research indicate a significant difference in the shifts in the functional state during the movement along the routes for both groups. When the lighting changes from the beginning of the evening twilight, the energy consumption by drivers increases by 40%. At the same time, the maximum rate of energy consumption for the driver is 12.6 kJ/min, i.e. 2.9 kcal/min. Also, drivers with less driving experience were less attentive and more emotionally stressed. At the same time, the drivers of both groups experienced significant difficulties in reducing the lighting in the event of conflicting situations while driving. At the same time, energy expenditure significantly exceeded the limit indicators, and attention indicators worsened. This fact was observed after 15-20 minutes of driving in the evening twilight. The experimentally obtained data on changes in the driver’s attention index in the conditions of evening twilight can be used to build multifactorial models taking into account traffic parameters on different categories of streets. In the conditions of the evening twilight, the driver’s attention is significantly reduced. When the illumination changes from 50 lux (driving time 15-20 min.) and less, the driver experiences visual fatigue from constantly searching for objects on the road. At the same time, the number of fixations is significantly reduced, as the driver spends more time recognizing and searching for the necessary elements of the road environment, pedestrians, etc. Danger in the driver causes a state of stress, which is reflected in an increase in the driver’s breathing rate and, as a consequence, a change in the functional state. The main factor affecting the amount of energy spent in such conditions is the interaction of the driver with the road conditions. These factors require the driver to take certain actions when driving a car: acceleration, braking, maintaining speed and certain reactions in the event of a traffic hazard. The results of the analysis of parameters of the functional state of drivers with different years of work showed significant differences between electrophysiological indicators, both when moving in conditions of evening twilight and in background conditions. In the background conditions, lower values of electrophysiological indicators are primarily related to the different age of the drivers. Drivers with 3-5 years of driving experience had an average age of 25 years, and drivers with 10-16 years of driving experience had an average age of 34 years. Keywords: driver attention, traffic flow, human factor.

Monitoring Respiratory Rate Continuously Without Attaching a Sensor During a Challenging Ramped Protocol.

Respiratory rate (RR) is a crucial vital sign in patient monitoring and is often the best marker of the deterioration of a sick patient. It can be used to help diagnose numerous medical conditions and has been demonstrated to be an independent predictor of patient outcomes in various critical care settings and is incorporated in many clinical early warning scores. Here, we report on the performance of depth-camera-based system for the noncontact monitoring of RR during a ramped RR protocol. The ramped breathing protocol was developed specifically to test the relatively rapid changes in rates, which include clinically important low and high ranges of RRs. We performed a series of experimental runs with healthy volunteers who were instructed to breathe over a wide range of RRs, where the rates were ramped up from 4 breaths/min to 50 breaths/min then back down to 4 breaths/min in a series of ramped steps. Depth information was acquired from the scene and used to determine a respiratory rate (RRdepth), and this was compared to capnograph or spirometer respiratory rate reference (RRref). A total of 9,482 contemporaneous data pairs (RRdepth, RRref) were collected during the study for comparison. A Pearson correlation coefficient of 0.995 was achieved and a line of best fit given by RRdepth = 0.99 × RRref + 0.36 breaths/min. The overall root mean squared difference (RMSD) across the runs was 1.29 breaths/min with a corresponding bias of 0.16 breaths/min, respectively. The associated Bland-Altman analysis found limits of agreement of -2.45 and 2.75 breaths/min. When the data were subdivided according to low, medium, and high RRs, corresponding to ≤10, >10 to 20, and >20 breaths/min, the RMSD accuracies were found to be 0.94, 1.34, and 1.55 breaths/min, respectively. The technology performed well, exhibiting an RMSD accuracy well within our target of 3 breaths/min, both across the whole range and across each individual subrange. In summary, our results indicate the potential viability of continuous noncontact monitoring for the determination of RR over a clinically relevant range.

Wearable Masks Integrated with Conducting Polymer and Carbon-Based Nanomaterials for VOC and Breath Monitoring

There has been considerable effort to develop wearable electronics from life-supporting devices for solders to fashion accessories such as smartwatches. The research of gas sensors has also attempted to adapt wearables with the power of nanotechnology. On the wearable platform, miniature gas sensors will provide real-time information about the atmosphere to protect each personnel from possible hazardous chemical attacks. In addition, wearable gas sensors can be facilitated to monitor human’s breath as medical applications. [1] Continuous monitoring of respiratory rate in real-time can act as a crucial benchmark for non-invasively detecting cardiac or arterial vascular function. Anomalies in respiratory rate serve as a significant indicator of a patient's health deterioration. Furthermore, it has been reported that aberrations in respiratory rate, as a physiological parameter, can be utilized to monitor common COVID-19 symptoms [2]. The analysis of breath through identifying biomarkers in exhaled breath proves to be an effective method for assessing metabolic disorders or dysfunctions in the human body. Conducting polymers (CPs) have generated significant interest in constructing flexible gas sensors. The inherent issue with pure CPs lies in their tendency to deprotonate in the presence of air, leading to a notable decline in long-term stability. Furthermore, limited gas response on diverse gaseous species demands the exploration of hybridizing CPs and other nanomaterials. When nanomaterials are hybridized with CPs, a synergistic effect, which comes from the benefits of each material, can improve sensing performance.In this study, polyaniline (PANI) conducting polymer and carbon-based nanomaterials, including carbon nanotubes (CNTs), graphene, or MXene were synthesized in a composite form and deposited into disposable masks. The developed gas sensor demonstrates remarkable stability attributed to the presence of van der Waals forces and π–π interactions between carbon-based nanomaterials and polyaniline (PANI). This stability is crucial for its reliable performance over time. The sensor's sensitivity is notably improved due to enhanced charge transfer channels and a porous structure that facilitates the adsorption of target gas. Additionally, the composite sensors detect breathing patterns in real-time, which demonstrates noninvasive human breath monitoring. A noteworthy aspect is that a significant portion of breath signals originates from the presence of target gases and moisture in exhaled breath. Although volatile sulfur compounds, CO2, and volatile organic compounds in the exhaled breath have a minimal influence on the sensing signals, the sensor remains highly responsive to the key components, particularly target gas by selecting carbon-based nanomaterials [3, 4]. The proposed gas sensing mechanism of the CP-based composites is discussed.

WSe2/chitosan-based wearable multi-functional platform for monitoring electrophysiological signals, pulse rate, respiratory rate, and body movements.

Acleanroom free optimized fabrication of a low-cost facile tungsten diselenide (WSe2) combined with chitosan-based hydrogel device is reportedfor multifunctional applications including tactile sensing, pulse rate monitoring, respiratory rate monitoring, human body movements detection, and human electrophysiological signal detection. Chitosan being a natural biodegradable, non-toxic compound serves as a substrate to the semiconducting WSe2 electrode which is synthesized using a single step hydrothermal technique. Elaborate characterization studies are performed to confirm the morphological, structural, and electrical properties of the fabricated chitosan/WSe2 device. Chitosan/WSe2 sensor with copper contacts on each side is put directly on skin to capture human body motions. The resistivity of the sample was calculated as 26 kΩ m-1. The device behaves as an ultrasensitive pressure sensor for tactile and arterial pulse sensing with response time of 0.9s and sensitivity of around 0.02kPa-1. It is also capable for strain sensing with a gauge factor of 54 which is significantly higher than similar other reported electrodes. The human body movements sensing can be attributed to the piezoresistive character of WSe2 that originates from its non-centrosymmetric structure. Further, the sensor is employed for monitoring respiratory rate which measures to 13 counts/min for healthy individual and electrophysiological signals like ECG and EOG which can be used later for detecting numerous pathological conditions in humans. Electrophysiological signal sensing is carried out using a bio-signal amplifier (Bio-Amp EXG Pill) connected to Arduino. The skin-friendly, low toxic WSe2/chitosan dry electrodes pave the way for replacing wet electrodes and find numerous applications in personalized healthcare.

Continuous measurements of respiratory muscle blood flow and oxygen consumption using noninvasive frequency-domain near-infrared spectroscopy and diffuse correlation spectroscopy.

Prior studies of muscle blood flow and muscle-specific oxygen consumption have required invasive injection of dye and magnetic resonance imaging, respectively. Such measures have limited utility for continuous monitoring of the respiratory muscles. Frequency-domain near-infrared spectroscopy and diffuse correlation spectroscopy (FD-NIRS & DCS) can provide continuous surrogate measures of blood flow index (BFi) and metabolic rate of oxygen consumption (MRO2). This study aimed to validate sternocleidomastoid FD-NIRS & DCS outcomes against electromyography (EMG) and mouth pressure (Pm) during incremental inspiratory threshold loading (ITL). Six female and six male healthy adults (means ± SD; 30 ± 7 yr, maximum inspiratory pressure 118 ± 61 cmH2O) performed incremental ITL starting at low loads (8 ± 2 cmH2O) followed by 50-g increments every 2 min until task failure. FD-NIRS & DCS continuously measured sternocleidomastoid oxygenated and deoxygenated hemoglobin + myoglobin (oxy/deoxy[Hb + Mb]), tissue saturation of oxygen (StO2), BFi, and MRO2. Ventilatory parameters including inspiratory Pm were also evaluated. Pm increased during incremental ITL (P < 0.05), reaching -47[-74 to -34] cmH2O (median [IQR: 25%-75%]) at task failure. Ventilatory parameters were constant throughout ITL (all P > 0.05). Sternocleidomastoid BFi and MRO2 increased from the start of the ITL (both P < 0.05). Deoxy[Hb + Mb] increased close to task failure, concomitantly with a constant increase in MRO2, and decreased StO2. Sternocleidomastoid deoxy[Hb + Mb], BFi, StO2, and MRO2 obtained during ITL via FD-NIRS & DCS correlated with sternocleidomastoid EMG (all P < 0.05). In healthy adults, FD-NIRS & DCS can provide continuous surrogate measures of respiratory BFi and MRO2. Increasing sternocleidomastoid oxygen consumption near task failure was associated with increased oxygen extraction and reduced tissue saturation.NEW & NOTEWORTHY This study introduces a novel approach, frequency-domain near-infrared spectroscopy and diffuse correlation spectroscopy (FD-NIRS & DCS), for noninvasive continuous monitoring of respiratory muscle blood flow and metabolic rate of oxygen consumption. Unlike prior methods involving invasive dye injection and magnetic resonance imaging, FD-NIRS & DCS offers the advantage of continuous measurement without the need for invasive procedures. It holds promise for advancing muscle physiology understanding and opens avenues for real-time monitoring of respiratory muscles.

An anesthetized rat assay for evaluating the effects of organophosphate-based compounds and countermeasures on intracranial EEG and respiratory rate

The development of medical countermeasures (MCMs) against organophosphate (OP) induced poisoning is of substantial importance. Use of conventional therapeutics is complicated by off-target effects and restricted penetration of the blood-brain barrier (BBB). Therefore, a concerted effort is underway to discover improved acetylcholinesterase (AChE) reactivators, muscarinic acetylcholine receptor (mAChR) antagonists, and other countermeasures with broader spectrum activity and enhanced CNS efficacy. We recently developed a rat brain slice assay to assess the efficacy of AChE reactivators and mAChR antagonists against the acute effects of the organophosphorus AChE inhibitor 4-nitrophenyl isopropyl methylphosphonate (NIMP) in the basolateral amygdala (BLA). Here we introduce a complimentary anesthetized animal model to evaluate the same compounds in vivo with concurrent monitoring of EEG and respiratory rate. We find that intravenous delivery of 0.5 mg/kg NIMP reliably produces seizure like activity in the BLA, with concurrent respiratory depression and eventual respiratory failure. The central effects of AChE reactivators and mAChR antagonists delivered intravenously are consistent with their expected ability to cross the BBB. Combining our previously described in vitro assay with the methods described here provides a relatively comprehensive set of preclinical tools for evaluating the efficacy of novel MCMs. Notably, using these methods is expected to reduce the need to subject conscious animals to cholinergic crises, which aligns with the AAALAC’s 3Rs principle of refinement.

Prospective observational study of 2 wearable strain sensors for measuring the respiratory rate.

The respiratory rate is an important factor for assessing patient status and detecting changes in the severity of illness. Real-time determination of the respiratory rate will enable early responses to changes in the patient condition. Several methods of wearable devices have enabled remote respiratory rate monitoring. However, gaps persist in large-scale validation, patient-specific calibration, standardization and their usefulness in clinical practice has not been fully elucidated. The aim of this study was to evaluate the accuracy of 2 wearable stretch sensors, C-STRECH® which is used in clinical practice and a novel stretchable capacitor in measuring the respiratory rate. The respiratory rate of 20 healthy subjects was measured by a spirometer with the stretch sensor applied to 1 of 5 locations (umbilicus, lateral abdomen, epigastrium, lateral chest, or chest) of their body at rest while they were in a sitting or supine position before or after exercise. The sensors detected the largest amplitudes at the epigastrium and umbilicus compared to other sites of measurement for the sitting and supine positions, respectively. At rest, the respiratory rate of the sensors had an error of 0.06 to 2.39 breaths/minute, whereas after exercise, an error of 1.57 to 3.72 breaths/minute was observed compared to the spirometer. The sensors were able to detect the respiratory rate of healthy volunteers in the sitting and supine positions, but there was a need for improvement in detection after exercise.

Lab-on-Fiber Humidity Sensor for Real-Time Respiratory Rate Monitoring

Lab-on-Fiber Humidity Sensor for Real-Time Respiratory Rate Monitoring

Real-World Effectiveness of Ensitrelvir in Reducing Severe Outcomes in Outpatients at High Risk for COVID-19.

This study aimed to evaluate the effectiveness of ensitrelvir, an oral antiviral, in reducing hospitalization risk in outpatients at high-risk for severe COVID-19 during the Omicron era. This was a retrospective study using a large Japanese health insurance claims database. It included high-risk outpatients for severe symptoms who received their first COVID-19 diagnosis between November 2022 and July 2023. The study included outpatients aged≥18years. The primary endpoint was all-cause hospitalization during the 4-week period from the date of outpatient diagnosis and medication, comparing the ensitrelvir group (n=5177) and the no antiviral treatment group (n=162,133). The risk ratio and risk difference were evaluated after adjusting patient background distribution by the inverse probability of treatment weight (IPTW) method. Secondary endpoints were incidence of respiratory and heart rate monitoring, oxygen therapy, ventilator use, intensive care admission, and all-cause death. The risk ratio for all-cause hospitalization between the ensitrelvir group (n=167,385) and the no antiviral treatment group (n=167,310) after IPTW adjustment was 0.629 [95% confidence interval (CI) 0.420, 0.943]. The risk difference was -0.291 [95% CI -0.494, -0.088]. The incidence of both respiratory and heart rate monitoring and oxygen therapy was lower in the ensitrelvir group. Ventilator use, intensive care admission, and all-cause death were difficult to assess because of the limited events. The incidence of all-cause hospitalization was significantly lower in the ensitrelvir group than in the no antiviral treatment group, suggesting ensitrelvir is an effective treatment in patients at risk of severe COVID-19.

Energy-Efficient PPG-Based Respiratory Rate Estimation Using Spiking Neural Networks.

Respiratory rate (RR) is a vital indicator for assessing the bodily functions and health status of patients. RR is a prominent parameter in the field of biomedical signal processing and is strongly associated with other vital signs such as blood pressure, heart rate, and heart rate variability. Various physiological signals, such as photoplethysmogram (PPG) signals, are used to extract respiratory information. RR is also estimated by detecting peak patterns and cycles in the signals through signal processing and deep-learning approaches. In this study, we propose an end-to-end RR estimation approach based on a third-generation artificial neural network model-spiking neural network. The proposed model employs PPG segments as inputs, and directly converts them into sequential spike events. This design aims to reduce information loss during the conversion of the input data into spike events. In addition, we use feedback-based integrate-and-fire neurons as the activation functions, which effectively transmit temporal information. The network is evaluated using the BIDMC respiratory dataset with three different window sizes (16, 32, and 64 s). The proposed model achieves mean absolute errors of 1.37 ± 0.04, 1.23 ± 0.03, and 1.15 ± 0.07 for the 16, 32, and 64 s window sizes, respectively. Furthermore, it demonstrates superior energy efficiency compared with other deep learning models. This study demonstrates the potential of the spiking neural networks for RR monitoring, offering a novel approach for RR estimation from the PPG signal.

Contactless vital signs monitoring in macaques using a mm-wave FMCW radar

Heart rate (HR) and respiration rate (RR) play an important role in the study of complex behaviors and their physiological correlations in non-human primates (NHPs). However, collecting HR and RR information is often challenging, involving either invasive implants or tedious behavioral training, and there are currently few established simple and non-invasive techniques for HR and RR measurement in NHPs owing to their stress response or indocility. In this study, we employed a frequency-modulated continuous wave (FMCW) radar to design a novel contactless HR and RR monitoring system. The designed system can estimate HR and RR in real time by placing the FMCW radar on the cage and facing the chest of both awake and anesthetized macaques, the NHP investigated in this study. Experimental results show that the proposed method outperforms existing methods, with averaged absolute errors between the reference monitor and radar estimates of 0.77 beats per minute (bpm) and 1.29 respirations per minute (rpm) for HR and RR, respectively. In summary, we believe that the proposed non-invasive and contactless estimation method could be generalized as a HR and RR monitoring tool for NHPs. Furthermore, after modifying the radar signal-processing algorithms, it also shows promise for applications in other experimental animals for animal welfare, behavioral, neurological, and ethological research.