Critical review of non-invasive respiratory monitoring in medical care

Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration

Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.

Wearable Systems for Unveiling Collective Intelligence in Clinical Settings

Nowadays, there is an ever-growing interest in assessing the collective intelligence (CI) of a team in a wide range of scenarios, thanks to its potential in enhancing teamwork and group performance. Recently, special attention has been devoted on the clinical setting, where breakdowns in teamwork, leadership, and communication can lead to adverse events, compromising patient safety. So far, researchers have mostly relied on surveys to study human behavior and group dynamics; however, this method is ineffective. In contrast, a promising solution to monitor behavioral and individual features that are reflective of CI is represented by wearable technologies. To date, the field of CI assessment still appears unstructured; therefore, the aim of this narrative review is to provide a detailed overview of the main group and individual parameters that can be monitored to evaluate CI in clinical settings, together with the wearables either already used to assess them or that have the potential to be applied in this scenario. The working principles, advantages, and disadvantages of each device are introduced in order to try to bring order in this field and provide a guide for future CI investigations in medical contexts.

An ultra-small integrated CO2 infrared gas sensor for wearable end-tidal CO2 monitoring

Human physiological metabolic status can be obtained by monitoring exhaled CO2 concentration, but current CO2 sensors have disadvantages such as large size, high power consumption, and slow response time, which limit their application in wearable devices and portable instruments. In this article, we report a small size, good performance, and large range CO2 infrared gas sensor that integrates a high emissivity MEMS emitter chip, a high detectivity thermopile chip, and a high coupling efficiency optical chamber to achieve high efficiency optical-thermal-electrical conversion. Compared with typical commercial sensors, the size of the sensor can be reduced by approximately 80% to only 10 mm × 10 mm × 6.5 mm, with the advantages of low power consumption and fast response speed. Further, a monitoring system for end-tidal CO2 concentration installed on a mask was developed using this sensor, and good results were achieved.

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events.

Preferred surface orientation for CO oxidation on SnO2 surfaces

In the present study, we perform a comparative study on the oxidation mechanism of CO gas molecules on SnO2 (110), (101), and (100) surfaces. The optimized adsorption configurations show that the adsorption of CO molecules could occur similarly on the three SnO2 surfaces via two adsorption modes, physisorption of CO on the Sn5c site that is considered as the first step for CO oxidation, followed by CO chemisorption on the O2c site resulting in the formation of CO2 species. Based on the calculated adsorption energies and CO molecule diffusion on SnO2 surfaces, CO molecule adsorption on the (101) surface exhibits the highest adsorption energy and the lowest reaction barrier for CO oxidation compared to the widely considered (110) surface or the (100) surface. These findings are expected to have a major impact on improving sensing properties toward toxic gas by means of surface-orientation engineering.

Predicting Respiratory Rate from Electrocardiogram and Photoplethysmogram Using a Transformer-Based Model

The respiratory rate (RR) serves as a critical physiological parameter in the context of both diagnostic and prognostic evaluations. Due to the challenges of direct measurement, RR is still predominantly measured through the traditional manual counting-breaths method in clinic practice. Numerous algorithms and machine learning models have been developed to predict RR using physiological signals, such as electrocardiogram (ECG) or/and photoplethysmogram (PPG) signals. Yet, the accuracy of these existing methods on available datasets remains limited, and their prediction on new data is also unsatisfactory for actual clinical applications. In this paper, we proposed an enhanced Transformer model with inception blocks for predicting RR based on both ECG and PPG signals. To evaluate the generalization capability on new data, our model was trained and tested using subject-level ten-fold cross-validation using data from both BIDMC and CapnoBase datasets. On the test set, our model achieved superior performance over five popular deep-learning-based methods with mean absolute error (1.2) decreased by 36.5% and correlation coefficient (0.85) increased by 84.8% compared to the best results of these models. In addition, we also proposed a new pipeline to preprocess ECG and PPG signals to improve model performance. We believe that the development of the TransRR model is expected to further expedite the clinical implementation of automatic RR estimation.

Evaluation Protocol for Analogue Intelligent Medical Radars: Towards a Systematic Approach Based on Theory and a State of the Art

We propose the basis for a systematised approach to the performance evaluation of analogue intelligent medical radars. In the first part, we review the literature on the evaluation of medical radars and compare the provided experimental elements with models from radar theory in order to identify the key physical parameters that will be useful to develop a comprehensive protocol. In the second part, we present our experimental equipment, protocol and metrics to carry out such an evaluation.

Clinical validation of a wearable respiratory rate device: A brief report

BACKGROUND

Respiratory rate (RR) is used for the diagnosis and management of medical conditions and can predict clinical changes. Heavy workload, understaffing, and errors related to poor recording make it underutilized. Wearable devices may facilitate its use.

METHODS

RR measurements using a wearable photoplethysmography-based monitor were compared with medical grade devices in complementary clinical scenarios: Study one included a comparison to a capnograph in 35 healthy volunteers; Study two included a comparison to a ventilator monitor in 18 ventilated patients; and Study three included a comparison to capnograph in 92 COVID-19 patients with active pulmonary disease. Pearson's correlations and Bland-Altman analysis were used to assess the accuracy and agreement between the measurement techniques, including stratification for Body Mass Index (BMI) and skin tone. Statistical significance was set at p ≤ 0.05.

RESULTS

High correlation was found in all studies (r = 0.991, 0.884, and 0.888, respectively, p < 0.001 for all). 95% LOA of ±2.3, 1.7-(-1.6), and ±3.9 with a bias of < 0.1 breaths per minute was found in Bland-Altman analysis in studies 1,2, and 3, respectively. In all, high accordance was found in all sub-groups.

CONCLUSIONS

RR measurements using the wearable monitor were highly-correlated with medical-grade devices in various clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov, https://clinicaltrials.gov/ct2/show/NCT03603860.

Wearable bioelectronic masks for wireless detection of respiratory infectious diseases by gaseous media

Respiratory infectious diseases (H1N1, H5N1, COVID-19, etc.) are pandemics that can continually spread in the air through micro-droplets or aerosols. However, the detection of samples in gaseous media is hampered by the requirement for trace amounts and low concentrations. Here, we develop a wearable bioelectronic mask device integrated with ion-gated transistors. Based on the sensitive gating effect of ion gels, our aptamer-functionalized transistors can measure trace-level liquid samples (0.3 μL) and even gaseous media samples at an ultra-low concentration (0.1 fg/mL). The ion-gated transistor with multi-channel analysis can respond to multiple targets simultaneously within as fast as 10 min, especially without sample pretreatment. Integrating a wireless internet of things system enables the wearable mask to achieve real-time and on-site detection of the surrounding air, providing an alert before infection. The wearable bioelectronic masks hold promise to serve as an early warning system to prevent outbreaks of respiratory infectious diseases.

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.

Bioinspired Stretchable Transducer for Wearable Continuous Monitoring of Respiratory Patterns in Humans and Animals

A bio-inspired continuous wearable respiration sensor modeled after the lateral line system of fish is reported which is used for detecting mechanical disturbances in the water. Despite the clinical importance of monitoring respiratory activity in humans and animals, continuous measurements of breathing patterns and rates are rarely performed in or outside of clinics. This is largely because conventional sensors are too inconvenient or expensive for wearable sensing for most individuals and animals. The bio-inspired air-silicone composite transducer (ASiT) is placed on the chest and measures respiratory activity by continuously measuring the force applied to an air channel embedded inside a silicone-based elastomeric material. The force applied on the surface of the transducer during breathing changes the air pressure inside the channel, which is measured using a commercial pressure sensor and mixed-signal wireless electronics. The transducer produced in this work are extensively characterized and tested with humans, dogs, and laboratory rats. The bio-inspired ASiT may enable the early detection of a range of disorders that result in altered patterns of respiration. The technology reported can also be combined with artificial intelligence and cloud computing to algorithmically detect illness in humans and animals remotely, reducing unnecessary visits to clinics.

The Effect of Walking on the Estimation of Breathing Pattern Parameters using Wearable Bioimpedance

Wearable bioimpedance is a technique proposed to estimate breathing parameters such as respiratory rate (RR). However, its potential application lies in clinical investigation of daily-life activities like walking. This study evaluated the effect of the walking interference on the estimation of breathing parameters. 50 chronic obstructive pulmonary disease patients performed static and active measurements during thoracic bioimpedance acquisition. The static measurements included respiratory airflow for reference. The active measurements were used to estimate the walking interference from bioimpedance, and the obtained signals were added to static measurements for comparison with the reference. Afterward, we applied four different preprocessing methods to remove this walking interference and the resulting signals were used to detect the respiratory cycles and estimate breathing parameters (inspiratory time, expiratory time, duty cycle, and RR). The methods performed differently in terms of accuracy and mean average percentage error (MAPE), showing the need for specific preprocessing for active measurements. Furthermore, the MAPE values in the RR estimation were close to 3 % indicating that breathing parameters can be accurately estimated during walking. Accordingly, the present study reinforces the applicability of wearable bioimpedance for respiratory monitoring. Clinical relevance- This study exhibits the suitability of wearable bioimpedance to estimate accurate breathing param-eters during walking activities.

Portable Respiration Monitoring System with an Embroidered Capacitive Facemask Sensor

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.

Fast Lead-Free Humidity Sensor Based on Hybrid Halide Perovskite

An environmentally friendly analog of the prominent methylammonium lead halide perovskite, methylammonium bismuth bromide (MA3Bi2Br9), was prepared and investigated in the form of powder, single crystals and nanowires. Complete characterization via synchrotron X-ray diffraction data showed that the bulk crystal does not incorporate water into the structure. At the same time, water is absorbed on the surface of the crystal, and this modification leads to the changes in the resistivity of the material, thus making MA3Bi2Br9 an excellent candidate for use as a humidity sensor. The novel sensor was prepared from powder-pressed pellets with attached carbon electrodes and was characterized by being able to detect relative humidity over the full range (0.7–96% RH) at ambient temperature. Compared to commercial and literature values, the response and recovery times are very fast (down to 1.5 s/1.5 s).

A Wearable Breath Sensor Based on Fiber-Tip Microcantilever

Respiration rate is an essential vital sign that requires monitoring under various conditions, including in strong electromagnetic environments such as in magnetic resonance imaging systems. To provide an electromagnetically-immune breath-sensing system, we propose an all-fiber-optic wearable breath sensor based on a fiber-tip microcantilever. The microcantilever was fabricated on a fiber-tip by two-photon polymerization microfabrication based on femtosecond laser, so that a micro Fabry-Pérot (FP) interferometer was formed between the microcantilever and the end-face of the fiber. The cavity length of the micro FP interferometer was reduced as a result of the bending of the microcantilever induced by breath airflow. The signal of breath rate was rebuilt by detecting power variations of the FP interferometer reflected light and applying dynamic thresholds. The breath sensor achieved a high sensitivity of 0.8 nm/(m/s) by detecting the reflection spectrum upon applied flow velocities from 0.53 to 5.31 m/s. This sensor was also shown to have excellent thermal stability as its cross-sensitivity of airflow with respect to the temperature response was only 0.095 (m/s)/°C. When mounted inside a wearable surgical mask, the sensor demonstrated the capability to detect various breath patterns, including normal, fast, random, and deep breaths. We anticipate the proposed wearable breath sensor could be a useful and reliable tool for respiration rate monitoring.

Algorithmic insights of camera-based respiratory motion extraction

OBJECTIVE

Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for contactless health monitoring. The core algorithm for this application is the measurement of tiny chest/abdominal motions induced by respiration (i.e. capturing sub-pixel displacement caused by subtle motion between subsequent video frames), and the fundamental challenge is motion sensitivity. Though prior art reported on the validation with real human subjects, there is no thorough or rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms.

APPROACH

A set-up was designed with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified through the phantom benchmark.

MAIN RESULTS

With the variation of phantom motion intensity between 0.5 mm and 8 mm, the recommended approach obtains an average precision, recall, coverage and MAE of 88.1%, 91.8%, 95.5% and 2.1bpm in the day-light condition, and 81.7%, 90.0%, 93.9% and 4.4 bpm in the night condition.

SIGNIFICANCE

The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring. The limitations of this study stem from the used physical phantom that does not consider human factors like body shape, sleeping posture, respiratory diseases, etc., and the investigated scenario is focused on sleep monitoring, not including scenarios with a sitting or standing patient like in clinical ward and triage.

An Analysis of Respiration with the Smart Sensor SENSIRIB in Patients Undergoing Thoracic Surgery

The paper examines the problem of respiration monitoring with easily wearable instrumentation by using a smart device that is properly designed and implemented with small and light components. The practical implementation is presented both in practical aspects and from experimental results by following a properly defined method with a medical-like protocol and specific procedure of testing. The results of a statistically significant campaign of experimental tests are reported with the characteristic data from the angles and acceleration components of a sensed rib both to validate the smart device and the procedure for respiration monitoring.

Respiratory Monitoring by Ultrafast Humidity Sensors with Nanomaterials: A Review

Respiratory monitoring is a fundamental method to understand the physiological and psychological relationships between respiration and the human body. In this review, we overview recent developments on ultrafast humidity sensors with functional nanomaterials for monitoring human respiration. Key advances in design and materials have resulted in humidity sensors with response and recovery times reaching 8 ms. In addition, these sensors are particularly beneficial for respiratory monitoring by being portable and noninvasive. We systematically classify the reported sensors according to four types of output signals: impedance, light, frequency, and voltage. Design strategies for preparing ultrafast humidity sensors using nanomaterials are discussed with regard to physical parameters such as the nanomaterial film thickness, porosity, and hydrophilicity. We also summarize other applications that require ultrafast humidity sensors for physiological studies. This review provides key guidelines and directions for preparing and applying such sensors in practical applications.

Simultaneous measurement of two biological signals using a multi-layered polyvinylidene fluoride sensor

Cardiovascular and deep breathing diseases can be detected by measuring human signals such as heart rate, respiration, and blood pressure, which are important physiological parameters for accessing the state of the body. However, conventionally, heart and respiration rates are monitored using different sensors, which is cumbersome and can further increase the psychological burden on patients. To address these issues, this report proposes a sensor consisting of two stacked elements that can simultaneously measure heart and respiration rates. The two signals received can be expressed separately as heart and respiration rates after signal processing. The two stacked elements are composed of polyvinylidene fluoride thin film bonded to a polydimethylsiloxane substrate. One element (element 1) measures movement related to the heart, and the other (element 2) measures movement related to breathing. Elements 1 and 2 were experimentally observed to have sensitivities of 0.163 V/N and 0.209 V/N, respectively. In addition, the proposed system was compared with a commercial digital heart rate and respiration rate measurement instrument and was verified to be effective for simultaneous measurement of human vital signals with multiple sensors. In addition, the proposed system is flexible, lightweight, and inexpensive, making it convenient and economical.

Estimation of Tidal Volume Using Load Cells on a Hospital Bed

Although respiratory failure is one of the primary causes of admission to intensive care, the importance placed on measurement of respiratory parameters is commonly overshadowed compared to cardiac parameters. With the increased demand for unobtrusive yet quantifi- able respiratory monitoring, many technologies have been proposed recently. However, there are challenges to be addressed for such technologies to enable widespread use. In this work, we explore the feasibility of using load cell sensors embedded on a hospital bed for monitoring respi- ratory rate (RR) and tidal volume (TV). We propose a globalized machine learning (ML)-based algorithm for estimating TV without the requirement of subject-specific calibration or training. In a study of 15 healthy subjects performing respiratory tasks in four different postures, the outputs from four load cell channels and the reference spirometer were recorded simultaneously. A signal processing pipeline was implemented to extract features that capture respira- tory movement and the respiratory effects on the cardiac (i.e., ballistocardiogram, BCG) signals. The proposed RR estimation algorithm achieved a root mean square error (RMSE) of 0.6 breaths per minute (brpm) against the ground truth RR from the spirometer. The TV estimation results demonstrated that combining all three axes of the low- frequency force signals and the BCG heartbeat features best quantifies the respiratory effects of TV. The model resulted in a correlation and RMSE between the estimated and true TV values of 0.85 and 0.23 L, respectively, in the posture independent model without electrocardiogram (ECG) signals. This study suggests that load cell sensors already existing in certain hospital beds can be used for convenient and continuous respiratory monitoring in general care settings.

Wireless Wearable Ultrasound Sensor to Characterize Respiratory Behavior

A wireless wearable sensor on a paper substrate was used to continuously monitor respiratory behavior that can extract and deliver clinically relevant respiratory parameters to a smartphone. Intended to be placed horizontally at the midpoint of the costal margin and the xiphoid process as determined through anatomical analysis and experimental test, the wearable sensor is compact at only 40 × 35 × 6 mm³ in size and 6.5 g weight including a 2.7 g lithium battery. The wearable sensor, consisting of an ultrasound emitter, an ultrasound receiver, wireless transmission system, and associated data acquisition, measures the linear change in circumference at the attachment location by recording and analyzing the changes in ultrasound pressure as the distance between the emitter and the receiver changes. Changes in ultrasound pressure corresponding to linear strain are converted to temporal lung volume data and are wirelessly transmitted to an associated custom-designed smartphone app. Processing the received data, the mobile app is able to display the temporal volume trace and the flow rate vs. volume loop graphs, which are standard plots used to analyze respiration. From the plots, the app is able to extract and display clinically relevant respiration parameters, including forced expiratory volume delivered in the first second of expiration (FEV₁) and forced vital capacity (FVC). The sensor was evaluated with eight volunteers, showing a mean difference of the FEV₁/FVC ratio as bounded by 0.00-4.25% when compared to the industry-standard spirometer results. By enabling continuous tracking of respiratory behavioral parameters, the wireless wearable sensor helps monitor the progression of chronic respiratory illnesses, including providing warnings to asthma patients and caregivers to pursue necessary medical assistance.

Towards Estimation of Tidal Volume and Respiratory Timings via Wearable-Patch-Based Impedance Pneumography in Ambulatory Settings

OBJECTIVE

Evaluating convenient, wearable multi-frequency impedance pneumography (IP) based respiratory monitoring in ambulatory persons with novel electrode positioning.

METHODS

A wearable multi-frequency IP system was utilized to estimate tidal volume (TV) and respiratory timings in 14 healthy subjects. A 5.1 cm 5.1 cm tetrapolar electrode array, affixed to the sternum, and a conventional thoracic electrode configuration were employed to measure the respective IP signals, patch and thoracic IP. Data collected during static posturessitting and supineand activitieswalking and stair-steppingwere evaluated against a simultaneously-obtained spirometer (SP) volume signal.

RESULTS

Across all measurements, estimated TV obtained from the patch and thoracic IP maintained a Pearson correlation coefficient (r) of 0.930.05 and 0.950.05 to the ground truth TV, respectively, with an associated root-mean-square error (RMSE) of 0.177 L and 0.129 L, respectively. Average respiration rates (RRs) were extracted from 30-second segments with mean-absolute-percentage errors (MAPEs) of 0.93% and 0.74% for patch and thoracic IP, respectively. Likewise, average inspiratory and expiratory timings were identified with MAPEs less than 6% and 4.5% for patch and thoracic IP, respectively.

CONCLUSION

We demonstrated that patch IP performs comparably to traditional, cumbersome IP configurations. We also present for the first time, to the best of our knowledge, that IP can robustly estimate breath-by-breath TV and respiratory timings during ambulation.

SIGNIFICANCE

This work represents a notable step towards pervasive wearable ambulatory respiratory monitoring via the fusion of a compact chest-worn form factor and multi-frequency IP that can be readily adapted for holistic cardiopulmonary monitoring.

Cost-effective vital signs monitoring system for COVID-19 patients in smart hospital

The lack of staffing during COVID-19 pandemic drives hospitals to expand their facilities in non-traditional settings to include centralized communication systems to monitor the vital signs of patients and predictive models to identify their health conditions. In this research, we have developed a microcontroller-based wireless vital signs monitoring system, which is able to measure the body temperature, heart rate, blood oxygen level, respiratory rate and Electrocardiogram of the patients. We managed to obtain a reliable but more affordable vital signs monitor with high mobility that can be implemented in large hospitals. The system satisfies the design considerations of medical centers in terms of size, cost, power consumption and simplicity in implementation. The developed system consists of a set of wearable sensor nodes, wireless communications infrastructure with multiple communications techniques to carry vital data from the patients to the management system that handles the patient’s medical data, and a graphical user interface with a control system that enables the hospital staff to observe the status of all the patients and take the appropriate actions. The system was implemented using 40 sensor nodes, 4 distribution points and one gateway covering a hospital area of approximately 2500 m². The system was tested and the measured percentage of lost packets is found to be less than 3.3% of those sent. During transmission, the current measured from the sensor node was 10.5 mA with a 3.3 V input voltage, which prolonged the operating time of the battery used.

Detection of Respiratory Phases to Estimate Breathing Pattern Parameters using Wearable Bioimpendace

Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance- This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters.

Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography

The current diagnostic procedures for assessing physiological response to exercise comprise blood lactates measurements, ergospirometry, and electrocardiography. The first is not continuous, the second requires specialized equipment distorting natural breathing, and the last is indirect. Therefore, we decided to perform the feasibility study with impedance pneumography as an alternative technique. We attempted to determine points in respiratory-related signals, acquired during stress test conditions, that suggest a transition similar to the gas exchange threshold. In addition, we analyzed whether or not respiratory activity reaches steady states during graded exercise. Forty-four students (35 females), practicing sports on different levels, performed a graded exercise test until exhaustion on cycloergometer. Eventually, the results from 34 of them were used. The data were acquired with Pneumonitor 2. The signals demonstrated that the steady state phenomenon is not as evident as for heart rate. The results indicated respiratory rate approaches show the transition point at the earliest (more than 6 min before the end of the exercise test on average), and the tidal volume ones at the latest (less than 5 min). A combination gave intermediate findings. The results showed the impedance pneumography appears reasonable for the transition point estimation, but this should be further studied with the reference.

Investigation of a Capnometry Guided Respiratory Intervention in the Treatment of Posttraumatic Stress Disorder

Evidence‐based treatments for posttraumatic stress disorder (PTSD), including psychotherapies and medications, have high dropout and nonresponse rates, suggesting that more acceptable and effective treatments for PTSD are needed. Capnometry Guided Respiratory Intervention (CGRI) is a digital therapeutic effective in panic disorder that measures and displays end-tidal carbon dioxide (EtCO₂) and respiratory rate (RR) in real-time within a structured breathing protocol and may have benefit in PTSD by moderating breathing and EtCO₂ levels. We conducted a single-arm study of a CGRI system, Freespira®, to treat symptoms of PTSD. Participants with PTSD (n = 55) were treated for four weeks with twice-daily, 17-min at-home CGRI sessions using a sensor and tablet with pre-loaded software. PTSD and associated symptoms were assessed at baseline, end-of treatment, 2-months and 6-months post-treatment. Primary efficacy outcome was 50% of participants having ≥ 6-point decrease in Clinician Administered PTSD Scale (CAPS-5) score at 2-month follow up. Tolerability, usability, safety, adherence and patient satisfaction were assessed. CGRI was well tolerated, with 88% [95% CI 74–96%] having ≥ 6-point decrease in CAPS-5 scores at 2-months post-treatment follow up. Mean CAPS-5 scores decreased from 49.5 [s.d. = 9.2] at baseline to 27.1 [s.d. = 17.8] at 2-months post-treatment follow up. Respiratory rate decreased and EtCO₂ levels increased. Associated mental and physical health symptoms also improved. This CGRI intervention was safe, acceptable, and well-tolerated in improving symptoms in this study in PTSD. Further study against an appropriate comparator is warranted.

Trial registration Clinicaltrials.gov NCT#03039231.

Flexible, Highly Sensitive Paper-Based Screen Printed MWCNT/PDMS Composite Breath Sensor for Human Respiration Monitoring

Accurate measurement and monitoring of respiration is vital in patients affected by severe acute respiratory syndrome coronavirus - 2 (SARS-CoV-2). Patients with severe chronic diseases and pneumonia need continuous respiration monitoring and oxygenation support. Existing respiratory sensing techniques require direct contact with the human body along with expensive and heavy Holter monitors for continuous real-time monitoring. In this work, we propose a low-cost, non-invasive and reliable paper-based wearable screen printed sensor for human respiration monitoring as an effective alternative of existing sensing systems. The proposed sensor was fabricated using traditional screen printing of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) composite based interdigitated electrodes on paper substrate. The paper substrate was used as humidity sensing material of the sensor. The hygroscopic nature of paper during inhalation and exhalation causes a change in dielectric constant, which in turn changes the capacitance of the sensor. The composite interdigitated electrode configuration exhibited better response times with a rise time of 1.178s being recorded during exhalation and fall time of 0.88s during inhalation periods. The respiration rate of sensor was successfully examined under various breathing conditions such as normal breathing, deep breathing, workout, oral breathing, nasal breathing, fast breathing and slow breathing by employing it in a wearable mask, a mandatory wearable product during the current COVID-19 pandemic situation.Thus, the above proposed sensor may hold tremendous potential in wearable/flexible healthcare technology with good sensitivity, stability, biodegradability and flexibility at this time of need.

Estimation of Respiratory Rate from Thermography Using Respiratory Likelihood Index

Respiration is a key vital sign used to monitor human health status. Monitoring respiratory rate (RR) under non-contact is particularly important for providing appropriate pre-hospital care in emergencies. We propose an RR estimation system using thermal imaging cameras, which are increasingly being used in the medical field, such as recently during the COVID-19 pandemic. By measuring temperature changes during exhalation and inhalation, we aim to track the respiration of the subject in a supine or seated position in real-time without any physical contact. The proposed method automatically selects the respiration-related regions from the detected facial regions and estimates the respiration rate. Most existing methods rely on signals from nostrils and require close-up or high-resolution images, while our method only requires the facial region to be captured. Facial region is detected using YOLO v3, an object detection model based on deep learning. The detected facial region is divided into subregions. By calculating the respiratory likelihood of each segmented region using the newly proposed index, called the Respiratory Quality Index, the respiratory region is automatically selected and the RR is estimated. An evaluation of the proposed RR estimation method was conducted on seven subjects in their early twenties, with four 15 s measurements being taken. The results showed a mean absolute error of 0.66 bpm. The proposed method can be useful as an RR estimation method.

Assessing the Tidal Volume through Wearables: A Scoping Review

The assessment of respiratory activity based on wearable devices is becoming an area of growing interest due to the wide range of available sensors. Accordingly, this scoping review aims to identify research evidence supporting the use of wearable devices to monitor the tidal volume during both daily activities and clinical settings. A screening of the literature (Pubmed, Scopus, and Web of Science) was carried out in December 2020 to collect studies: i. comparing one or more methodological approaches for the assessment of tidal volume with the outcome of a state-of-the-art measurement device (i.e., spirometry or optoelectronic plethysmography); ii. dealing with technological solutions designed to be exploited in wearable devices. From the initial 1031 documents, only 36 citations met the eligibility criteria. These studies highlighted that the tidal volume can be estimated by using different technologies ranging from IMUs to strain sensors (e.g., resistive, capacitive, inductive, electromagnetic, and optical) or acoustic sensors. Noticeably, the relative volumetric error of these solutions during quasi-static tasks (e.g., resting and sitting) is typically ≥10% but it deteriorates during dynamic motor tasks (e.g., walking). As such, additional efforts are required to improve the performance of these devices and to identify possible applications based on their accuracy and reliability.

Respiration Monitoring via Forcecardiography Sensors

In the last few decades, a number of wearable systems for respiration monitoring that help to significantly reduce patients’ discomfort and improve the reliability of measurements have been presented. A recent research trend in biosignal acquisition is focusing on the development of monolithic sensors for monitoring multiple vital signs, which could improve the simultaneous recording of different physiological data. This study presents a performance analysis of respiration monitoring performed via forcecardiography (FCG) sensors, as compared to ECG-derived respiration (EDR) and electroresistive respiration band (ERB), which was assumed as the reference. FCG is a novel technique that records the cardiac-induced vibrations of the chest wall via specific force sensors, which provide seismocardiogram-like information, along with a novel component that seems to be related to the ventricular volume variations. Simultaneous acquisitions were obtained from seven healthy subjects at rest, during both quiet breathing and forced respiration at higher and lower rates. The raw FCG sensor signals featured a large, low-frequency, respiratory component (R-FCG), in addition to the common FCG signal. Statistical analyses of R-FCG, EDR and ERB signals showed that FCG sensors ensure a more sensitive and precise detection of respiratory acts than EDR (sensitivity: 100% vs. 95.8%, positive predictive value: 98.9% vs. 92.5%), as well as a superior accuracy and precision in interbreath interval measurement (linear regression slopes and intercepts: 0.99, 0.026 s (R² = 0.98) vs. 0.98, 0.11 s (R² = 0.88), Bland–Altman limits of agreement: ±0.61 s vs. ±1.5 s). This study represents a first proof of concept for the simultaneous recording of respiration signals and forcecardiograms with a single, local, small, unobtrusive, cheap sensor. This would extend the scope of FCG to monitoring multiple vital signs, as well as to the analysis of cardiorespiratory interactions, also paving the way for the continuous, long-term monitoring of patients with heart and pulmonary diseases.

A thin-film temperature sensor based on a flexible electrode and substrate

Accurate temperature measurements can efficiently solve numerous critical problems and provide key information. Herein, a flexible micro-three-dimensional sensor, with a combination of platinum and indium oxide to form thermocouples, is designed and fabricated by a microfabrication process to achieve in situ real-time temperature measurements. The stability and reliability of the sensor are greatly improved by optimizing the process parameters, structural design, and preparation methods. A novel micro-three-dimensional structure with better malleability is designed, which also takes advantage of the fast response of a two-dimensional thin film. The as-obtained flexible temperature sensor with excellent stability and reliability is expected to greatly contribute to the development of essential components in various emerging research fields, including bio-robot and healthcare systems. The model of the application sensor in a mask is further proposed and designed to realize the collection of health information, reducing the number of deaths caused by the lack of timely detection and treatment of patients.

Tribo-Induced Smart Reflector for Ultrasensitive Self-Powered Wireless Sensing of Air Flow

Air-flow sensing is essential in broad applications of weather forecasting, ocean monitoring, gas leakage alarming, and health monitoring. However, in severe environments where electrical power supply and cable connection are not available, the sensing of air flow in a self-powered way is a challenging issue. In this work, we reported a tribo-induced smart reflector to achieve the self-powered wireless sensing of the air flow by combining an aerodynamics-driven triboelectric nanogenerator (TENG) and a silver-coated polymer network liquid crystal. Upon being driven by the air flow, the developed reflector performed specular and diffused reflectance without and with charging by the TENG, respectively, enabling wireless sensing through mechanical-electrical-optical signal conversion. In the developed sensing paradigm, the sensing module can be fully self-powered without the need of signal pre-amplification, which is electrically separated from the light source and detection modules without cable connections. The applications of self-powered wireless wind speed sensing and breath monitoring were performed to demonstrate the effectiveness of the developed paradigm toward self-powered wireless sensing nodes in the internet of things.

Awake Proning as an Adjunctive Therapy for Refractory Hypoxemia in Non-Intubated Patients with COVID-19 Acute Respiratory Failure: Guidance from an International Group of Healthcare Workers

Non-intubated patients with acute respiratory failure due to COVID-19 could benefit from awake proning. Awake proning is an attractive intervention in settings with limited resources, as it comes with no additional costs. However, awake proning remains poorly used probably because of unfamiliarity and uncertainties regarding potential benefits and practical application. To summarize evidence for benefit and to develop a set of pragmatic recommendations for awake proning in patients with COVID-19 pneumonia, focusing on settings where resources are limited, international healthcare professionals from high and low- and middle-income countries (LMICs) with known expertise in awake proning were invited to contribute expert advice. A growing number of observational studies describe the effects of awake proning in patients with COVID-19 pneumonia in whom hypoxemia is refractory to simple measures of supplementary oxygen. Awake proning improves oxygenation in most patients, usually within minutes, and reduces dyspnea and work of breathing. The effects are maintained for up to 1 hour after turning back to supine, and mostly disappear after 6-12 hours. In available studies, awake proning was not associated with a reduction in the rate of intubation for invasive ventilation. Awake proning comes with little complications if properly implemented and monitored. Pragmatic recommendations including indications and contraindications were formulated and adjusted for resource-limited settings. Awake proning, an adjunctive treatment for hypoxemia refractory to supplemental oxygen, seems safe in non-intubated patients with COVID-19 acute respiratory failure. We provide pragmatic recommendations including indications and contraindications for the use of awake proning in LMICs.

The Evaluation of a Noninvasive Respiratory Volume Monitor in Mechanically Ventilated Neonates and Infants

BACKGROUND

The respiratory volume monitor (RVM) (ExSpiron, Respiratory Motion Inc, Watertown, MA) uses thoracic impedance technology to noninvasively and continuously measure tidal volume (TV), respiratory rate (RR), and minute ventilation (MV). We aimed to validate the accuracy of the RVM to assess ventilation in neonates and infants by comparing it to spirometry.

METHODS

We used the RVM and Respironics NM3 spirometer (Respironics NM3 Respiratory Profile Monitor, Philips Healthcare, Amsterdam, the Netherlands) to record simultaneous and continuous measurements of MV, TV, and RR. The RVM measurements, with and without external calibration, were compared to the Respironics NM3 spirometer using Bland-Altman analysis. The relative errors (Bland-Altman) between RVM and Respironics NM3 were calculated and used to compute individual patient bias, precision, and accuracy as the mean error, the standard deviation (SD) of the error, and the root mean square error. Bland-Altman limits of agreement (LoA) were computed, and equivalence tests were performed.

RESULTS

Forty patients were studied to compare the RVM and Respironics NM3 measurements. The mean difference (ie, bias) for MV was 1.8% with 95% LoA, defined as mean ± 1.96 SD, in the range of -12.1% to 15.7%. Similarly, the mean difference (ie, bias) for TV and RR was 1.2% (95% LoA, -11.0% to 13.5%) and 0.6% (95% LoA, -3.7% to 5.0%), respectively. The mean measurement precision of the RVM relative to the Respironics NM3 for MV, TV, and RR was 10.8%, 8.9%, and 8.4%, respectively. The mean measurement accuracy for MV, TV, and RR across patients was 11.0%, 9.7%, and 7.1%, respectively.

CONCLUSIONS

The data demonstrate that the RVM measures TV and MV in this cohort with an average relative error of 11% when using patient calibration and 16.9% without patient calibration. The average relative error of RR was 7.1%. The RVM provides accurate measurement of RR, TV, and MV in mechanically ventilated neonates and infants.

Changes in quantifiable breathing pattern components predict asthma control: an observational cross-sectional study

Background

Breathing pattern disorders are frequently reported in uncontrolled asthma. At present, this is primarily assessed by questionnaires, which are subjective. Objective measures of breathing pattern components may provide additional useful information about asthma control. This study examined whether respiratory timing parameters and thoracoabdominal (TA) motion measures could predict and classify levels of asthma control.

Methods

One hundred twenty-two asthma patients at STEP 2- STEP 5 GINA asthma medication were enrolled. Asthma control was determined by the Asthma Control Questionnaire (ACQ7-item) and patients divided into ‘well controlled’ or ‘uncontrolled’ groups. Breathing pattern components (respiratory rate (RR), ratio of inspiration duration to expiration duration (Ti/Te), ratio of ribcage amplitude over abdominal amplitude during expiration phase (RCampe/ABampe), were measured using Structured Light Plethysmography (SLP) in a sitting position for 5-min. Breath-by-breath analysis was performed to extract mean values and within-subject variability (measured by the Coefficient of Variance (CoV%). Binary multiple logistic regression was used to test whether breathing pattern components are predictive of asthma control. A post-hoc analysis determined the discriminant accuracy of any statistically significant predictive model.

Results

Fifty-nine out of 122 asthma patients had an ACQ7-item < 0.75 (well-controlled asthma) with the rest being uncontrolled (n = 63). The absolute mean values of breathing pattern components did not predict asthma control (R² = 0.09) with only mean RR being a significant predictor (p < 0.01). The CoV% of the examined breathing components did predict asthma control (R² = 0.45) with all predictors having significant odds ratios (p < 0.01). The ROC curve showed that cut-off points > 7.40% for the COV% of the RR, > 21.66% for the CoV% of Ti/Te and > 18.78% for the CoV% of RCampe/ABampe indicated uncontrolled asthma.

Conclusion

The within-subject variability of timing parameters and TA motion can be used to predict asthma control. Higher breathing pattern variability was associated with uncontrolled asthma suggesting that irregular resting breathing can be an indicator of poor asthma control.

A Battery-Less Wireless Respiratory Sensor Using Micro-Machined Thin-Film Piezoelectric Resonators

In this work, we present a battery-less wireless Micro-Electro-Mechanical (MEMS)-based respiration sensor capable of measuring the respiration profile of a human subject from up to 2 m distance from the transceiver unit for a mean excitation power of 80 µW and a measured SNR of 124.8 dB at 0.5 m measurement distance. The sensor with a footprint of ~10 cm2 is designed to be inexpensive, maximize user mobility, and cater to applications where disposability is desirable to minimize the sanitation burden. The sensing system is composed of a custom UHF RFID antenna, a low-loss piezoelectric MEMS resonator with two modes within the frequency range of interest, and a base transceiver unit. The difference in temperature and moisture content of inhaled and exhaled air modulates the resonance frequency of the MEMS resonator which in turn is used to monitor respiration. To detect changes in the resonance frequency of the MEMS devices, the sensor is excited by a pulsed sinusoidal signal received through an external antenna directly coupled to the device. The signal reflected from the device through the antenna is then analyzed via Fast Fourier Transform (FFT) to extract and monitor the resonance frequency of the resonator. By tracking the resonance frequency over time, the respiration profile of a patient is tracked. A compensation method for the removal of motion-induced artifacts and drift is proposed and implemented using the difference in the resonance frequency of two resonance modes of the same resonator.

Feasibility of using a biofeedback device in mindfulness training - a pilot randomized controlled trial

Background

Stress can negatively impact an individual’s health and well-being and high levels of stress are noted to exist among college students today. While traditional treatment methods are plagued with stigma and transfer problems, newly developed wearable biofeedback devices may offer unexplored possibilities. Although these products are becoming commonplace and inexpensive, scientific evidence of the effectiveness of these products is scarce and their feasibility within research contexts are relatively unexplored. Conversely, companies are not required, and possibly reluctant, to release information on the efficacy of these products against their claims. Thus, in the present pilot, we assess the feasibility of using a real-time respiratory-based biofeedback device in preparation for a larger study. Our main aims were to assess device-adherence and collaboration with the company that develops and sells the device.

Method

Data were collected from 39 college students who self-identified as experiencing chronic stress at a Southwestern university in the USA. Students were randomized into either a mindfulness-only control group without a biofeedback device (n = 21), or an experimental group with biofeedback device (n = 18). Both groups received mindfulness meditation training. Pre-test and post-test procedures were conducted 2 weeks apart. Further, both participant compliance and company compliance were assessed and collaboration with the company was evaluated.

Results

Participant device-adherence as well as the company’s collaboration necessary for a full-scale study was determined to be low. This may also have affected our results which showed a strong main effect for time for all outcome variables, suggesting all groups showed improvement in their levels of stress after the intervention period. No group by time effects were identified, however, indicating no added benefit of the biofeedback device.

Conclusions

Our findings suggest feasibility of future studies requires full collaboration and detailed and agreed upon data sharing procedures with the biofeedback company. The particular device under investigation added no value to the intervention outcomes and it was not feasible to continue a larger-scale study. Further, as the technology sector is innovating faster than it can validate products, we urge for open science collaborations between public and private sectors to properly develop evidence-based regulations that can withstand technological innovation while maintaining product quality, safety, and effectiveness.

Trial registration

NCT02837016. Registered 19 July 2016.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40814-021-00807-1.

Breath emulator for simulation and modelling of expired tidal breath carbon dioxide characteristics

BACKGROUND

In this work we describe a breath emulator system, used to simulate temporal characteristics of exhaled carbon dioxide (CO₂) concentration waveform versus time simulating how much CO2 is present at each phase of the human lung respiratory process. The system provides a method for testing capnometers incorporating fast response non-dispersive infrared (NDIR) CO₂ gas sensing devices - in a clinical setting, capnography devices assess ventilation which is the CO₂ movement in and out of the lungs. A mathematical model describing the waveform of the expired CO₂ characteristic and influence of CO₂ gas sensor noise factors and speed of response is presented and compared with measured and emulated data.

OBJECTIVE

A range of emulated capnogram temporal waveforms indicative of normal and restricted respiratory function demonstrated. The system can provide controlled introduction of water vapour and/ or other gases, simulating the influence of water vapour in exhaled breath and presence of other gases in a clinical setting such as anaesthetic agents (eg N₂O). This enables influence of water vapour and/ or other gases to be assessed and modelled in the performance of CO₂ gas sensors incorporated into capnography systems. As such the breath emulator provides a means of controlled testing of capnometer CO₂ gas sensors in a non-clinical setting, allowing device optimisation before use in a medical environment.

METHODS

The breath emulator uses a unique combination of mass flow controllers, needle valves and a fast acting switchable pneumatic solenoid valve (FASV), used to controllably emulate exhaled CO₂ temporal waveforms for normal and restricted respiratory function. Output data from the described emulator is compared with a mathematical model using a range of input parameters such as time constants associated with inhalation/ exhalation for different parts of the respiratory cycle and CO₂ concentration levels. Sensor noise performance is modelled, taking into account input parameters such as sampling period, sensor temperature, sensing light throughput and pathlength.

RESULTS

The system described here produces realistic human capnographic waveforms and has the capability to emulate various waveforms associated with chronic respiratory diseases and early stage detection of exacerbations. The system has the capability of diagnosing medical conditions through analysis of CO₂ waveforms. Demonstrated in this work the emulator has been used to test NDIR gas sensor technology deployed in capnometer devices prior to formal clinical trialling.

Smart Recognition COVID-19 System to Predict Suspicious Persons Based on Face Features

The coronavirus (COVID-19) is identified at first in Wuhan in December 2019. The apparition of the COVID-19 virus is widely spread to concern all countries worldwide. The World Health Organization (WHO) on March 11 declare COVID-19 a pandemic. This Virus causes a serious infection of the respiratory system. Its high transmission constitutes great problems and challenges. The WHO proposes many actions to limit the spread of the virus such as quarantine and decrease or halt flights between states. The actions taken by states in airports are to detect suspicious persons with COVID-19. We aimed to provide a Computer-Aided Diagnosis (CAD) framework to predict suspicious COVID-19 person. This prediction identifies suspicious persons who suffer from shortness breath which is the main symptom of this disease. Extract shortness breath anomaly through the estimated heart rate from face based-video is the main contribution of the present paper. We developed a Smart Recognition COVID-19 (SRC) system to estimate the breath score. In conclusion, our study achieves an accurate breath score. The error is about 1 breath per minute. The proposed solution is of great importance because it helps managers in the airport to predict suspicious COVID-19 passengers.

A novel algorithm for minute ventilation estimation in remote health monitoring with magnetometer plethysmography

PURPOSE

The purpose of this study was to evaluate the accuracy of minute ventilation (V˙_E) estimation using a novel method based on a non-linear algorithm coupled with cycle-based features. The experiment protocol was well adapted for remote health monitoring applications by exploiting data streams from respiratory magnetometer plethysmography (RMP) during different physical activity (PA) types. Methods Thirteen subjects with an age distribution of 24.1±3.4 years performed thirteen PA ranging from sedentary to moderate intensity (walking at 4 and 6 km/h, running at 9 and 12 km/h, biking at 90 W and 110 W). In total, 3359 temporal segments of 10s were acquired using the Nomics RMP device while the iWorx spirometer was used for reference V˙_E measurements. An artificial neural network (ANN) model based on respiration features was used to estimate V˙_E and compared to the multiple linear regression (MLR) model. We also compared the subject-specific approach with the subject-independent approach. Results The ANN model using subject-specific approach achieved better accuracy for the V˙_E estimation. The bias was between 0.20±0.87 and 0.78±3 l/min with the ANN model as compared to 0.73±3.19 and 4.17±2.61 l/min with the MLR model. Conclusion Our results demonstrated the pertinence of processing data streams from wearable RMP device to estimate the V˙_E with sufficient accuracy for various PA types. Due to its low-complexity and real-time algorithm design, the current approach can be easily integrated into most remote health monitoring applications coupled with wearable sensors.

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.

Machine-learning enabled wireless wearable sensors to study individuality of respiratory behaviors

Respiratory behaviors provide useful measures of lung health. The current methods have limited capabilities of continuous characterization of respiratory behaviors, often required to assess respiratory disorders and diseases. This work presents a system equipped with a machine learning algorithm, capable of continuously monitoring respiratory behaviors. The system, consisting of two wireless wearable sensors, accurately extracts and classifies the features of respiratory behaviors of subjects within various postures, wirelessly transmitting the temporal respiratory behaviors to a laptop. The sensors were attached on the midway of the xiphoid process and the costal margin, and 1 cm above the umbilicus, respectively. The wireless wearable sensor, consisting of ultrasound emitter, ultrasound receiver, data acquisition and wireless transmitter, has a small footprint and light weight. The sensors correlate the mechanical strain at wearing sites to lung volume by measuring the local circumference changes of the chest and abdominal walls simultaneously. Eleven subjects were recruited to evaluate the wireless wearable sensors. Three different random forest classifiers, including generic, individual, and weighted-adaptive classifiers, were used to process the wireless data of the subjects at four different postures. The results demonstrate the respiratory behaviors are individual- and posture-dependent. The generic classifier merely reaches the accuracy of classifying postures of 21.9 ± 1.7% while individual and weighted-adaptive classifiers mark substantially high, up to 98.9 ± 0.6% and 98.8 ± 0.6%, respectively. The accurate monitoring of respiratory behaviors can track the progression of respiratory disorders and diseases, including chronic respiratory obstructive disease (COPD), asthma, apnea, and others for timely and objective approaches for control.

Respiratory Monitoring: Current State of the Art and Future Roads

In this article, we present current methodologies, available technologies, and demands for monitoring various respiratory parameters. We discuss the importance of noninvasive techniques for remote and continuous monitoring and challenges involved in the current "smart and connected health" era. We conducted an extensive literature review on the medical significance of monitoring respiratory vital parameters, along with the current methods and solutions with their respective advantages and disadvantages. We discuss the challenges of developing a noninvasive, wearable, wireless system that continuously monitors respiration parameters and opportunities in the field and then determines the requirements of a state-of-the-art system. Noninvasive techniques provide a significant amount of medical information for a continuous patient monitoring system. Contact methods offer more advantages than non-contact methods; however, reducing the size and power of contact methods is critical for enabling a wearable, wireless medical monitoring system. Continuous and accurate remote monitoring, along with other physiological data, can help caregivers improve the quality of care and allow patients greater freedom outside the hospital. Such monitoring systems could lead to highly tailored treatment plans, shorten patient stays at medical facilities, and reduce the cost of treatment.

Efficient video-based breathing pattern and respiration rate monitoring for remote health monitoring

A contact-free inexpensive measurement system with an algorithm based on the integral form of video frames is proposed to estimate the respiration rate from an extracted respiration pattern. The proposed algorithm is applied and tested on 28 videos of sleeping-simulated positions, and the results are compared with the manual visual inspection values. With linear regression, the determination coefficient (R²) is 0.961, which demonstrates high agreement with reference measurements. In addition, the Bland-Altman plot shows that almost all data points are within the 95% limits of agreement. Moreover, the time complexity of the proposed algorithm, which involves taking just a single point value of the integral image, is lower than that of traditional methods that circulate over a large number of points. In other words, the proposed algorithm achieves O(1) fixed-time complexity compared to O(N²) for traditional methods. The average speed of processing is enhanced by about 17.4%.

Review on Biomedical Sensors, Technologies and Algorithms for Diagnosis of Sleep Disordered Breathing: Comprehensive Survey

This paper provides a comprehensive review of available technologies for measurements of vital physiology related parameters that cause sleep disordered breathing (SDB). SDB is a chronic disease that may lead to several health problems and increase the risk of high blood pressure and even heart attack. Therefore, the diagnosis of SDB at an early stage is very important. The essential primary step before diagnosis is measurement. Vital health parameters related to SBD might be measured through invasive or non-invasive methods. Nowadays, with respect to increase in aging population, improvement in home health management systems is needed more than even a decade ago. Moreover, traditional health parameter measurement techniques such as polysomnography are not comfortable and introduce additional costs to the consumers. Therefore, in modern advanced self-health management devices, electronics and communication science are combined to provide appliances that can be used for SDB diagnosis, by monitoring a patient's physiological parameters with more comfort and accuracy. Additionally, development in machine learning algorithms provides accurate methods of analysing measured signals. This paper provides a comprehensive review of measurement approaches, data transmission, and communication networks, alongside machine learning algorithms for sleep stage classification, to diagnose SDB.

Ultracompact gas sensor with metal-organic-framework-based differential fiber-optic Fabry-Perot nanocavities

Refractive-index (RI)-based sensing is a major optical sensing modality that can be implemented in various spectral ranges. While it has been widely used for sensing of biochemical liquids, RI-based gas sensing, particularly small-molecule gases, is challenging due to the extremely small RI change induced by gas concentration variations. We propose a RI-based ultracompact fiber-optic differential gas sensor that employs metal-organic-framework (MOF)-based dual Fabry-Perot (FP) nanocavities. A MOF is used as the FP cavity material to enhance the sensitivity as well as the selectivity to particular gas molecules. The differential sensing scheme leverages the opposite change in the cavity-length-dependent reflection of the two FP cavities, which further enhances the sensitivity compared with single FP cavity based sensing. For proof-of-concept, a fiber-optic CO₂ sensor with ZIF-8-based dual FP nanocavities was fabricated. The effective footprint of the sensor was as small as 157 µm² and the sensor showed an enhanced sensitivity of 48.5 mV/CO₂Vol%, a dynamic range of 0-100 CO₂Vol%, and a resolution of 0.019 CO₂Vol% with 1 Hz low-pass filtering. Although the current sensor was only demonstrated for CO₂ sensing, the proposed sensor concept can be used for sensing of a variety of gases when different kinds of MOFs are utilized.

Combining Bioimpedance and Myographic Signals for the Assessment of COPD During Loaded Breathing

Chronic Obstructive Pulmonary Disease (COPD) is one of the most common chronic conditions. The current assessment of COPD requires a maximal maneuver during a spirometry test to quantify airflow limitations of patients. Other less invasive measurements such as thoracic bioimpedance and myographic signals have been studied as an alternative to classical methods as they provide information about respiration. Particularly, strong correlations have been shown between thoracic bioimpedance and respiratory volume. The main objective of this study is to investigate bioimpedance and its combination with myographic parameters in COPD patients to assess the applicability in respiratory disease monitoring. We measured bioimpedance, surface electromyography and surface mechanomyography in forty-three COPD patients during an incremental inspiratory threshold loading protocol. We introduced two novel features that can be used to assess COPD condition derived from the variation of bioimpedance and the electrical and mechanical activity during each respiratory cycle. These features demonstrate significant differences between mild and severe patients, indicating a lower inspiratory contribution of the inspiratory muscles to global respiratory ventilation in the severest COPD patients. In conclusion, the combination of bioimpedance and myographic signals provides useful indices to noninvasively assess the breathing of COPD patients.

Electrical Impedance Tomography for monitoring cardiac radiofrequency ablation: a scoping review of an emerging technology

Arrhythmias are common cardiac diseases which can be treated effectively by the cardiac radiofrequency ablation (CRFA). However, information regarding the lesion growth within the myocardium is critical to the procedure's safety and efficacy but still unavailable in the current catheterisation lab (CathLab). Over the last 20 years, many efforts have been made in order to track the lesion size during the procedure. Unfortunately, all the approaches have their own limitations preventing them from the clinical translation and hence making the lesion size monitoring during a CRFA still an open issue. Electrical Impedance Tomography (EIT) is an impedance imaging modality that might be able to image the thermal-related impedance changes from which the lesion size can be measured. With the availability of the patient's CT scans, for a detailed model, and the catheter-based electrodes for the internal electrodes, EIT accuracy and sensitivity to the ablated sites can be significantly improved and is worth being explored for this application. Though EIT is still new to CRFA with no in-vivo experiments being done according to our up-to-date searching, many related EIT studies and its extensive research in Hyperthermia and other ablations can reveal many hints for a possibility of the CRFA-EIT application. In this paper, we present a review on multiple aspects of EIT in CRFA. First, the expected CRFA-EIT signal range and frequency are discussed based on various measured impedance results obtained from lesions in the past. Second, the possible noise sources that can happen in a clinical CRFA procedure, along with their signal range and frequency compared to the CRFA-EIT signal, and, third, the available current solutions to separate such noises from the CRFA-EIT signal. Finally, we review the progress of EIT in thermal applications over the last two decades in order to identify the developments that EIT can take advantage of and the current drawbacks that need to be solved for a potential CRFA-EIT application.