Tidal Volume Estimation Research Articles

Tracking Tidal Volume From Holter and Wearable Armband Electrocardiogram Monitoring.

A novel method for tracking the tidal volume (TV) from electrocardiogram (ECG) is presented. The method is based on the amplitude of ECG-derived respiration (EDR) signals. Three different morphology-based EDR signals and three different amplitude estimation methods have been studied, leading to a total of 9 amplitude-EDR (AEDR) signals per ECG channel. The potential of these AEDR signals to track the changes in TV was analyzed. These methods do not need a calibration process. In addition, a personalized-calibration approach for TV estimation is proposed, based on a linear model that uses all AEDR signals from a device. All methods have been validated with two different ECG devices: a commercial Holter monitor, and a custom-made wearable armband. The lowest errors for the personalized-calibration methods, compared to a reference TV, were -3.48% [-17.41% / 12.93%] (median [first quartile / third quartile]) for the Holter monitor, and 0.28% [-10.90% / 17.15%] for the armband. On the other hand, medians of correlations to the reference TV were higher than 0.8 for uncalibrated methods, while they were higher than 0.9 for personal-calibrated methods. These results suggest that TV changes can be tracked from ECG using either a conventional (Holter) setup, or our custom-made wearable armband. These results also suggest that the methods are not as reliable in applications that induce small changes in TV, but they can be potentially useful for detecting large changes in TV, such as sleep apnea/hypopnea and/or exacerbations of a chronic respiratory disease.

Killer whale respiration rates.

Measuring breathing rates is a means by which oxygen intake and metabolic rates can be estimated to determine food requirements and energy expenditure of killer whales (Orcinus orca) and other cetaceans. This relatively simple measure also allows the energetic consequences of environmental stressors to cetaceans to be understood but requires knowing respiration rates while they are engaged in different behaviours such as resting, travelling and foraging. We calculated respiration rates for different behavioural states of southern and northern resident killer whales using video from UAV drones and concurrent biologging data from animal-borne tags. Behavioural states of dive tracks were predicted using hierarchical hidden Markov models (HHMM) parameterized with time-depth data and with labeled tracks of drone-identified behavioural states (from drone footage that overlapped with the time-depth data). Dive tracks were sequences of dives and surface intervals lasting ≥ 10 minutes cumulative duration. We calculated respiration rates and estimated oxygen consumption rates for the predicted behavioural states of the tracks. We found that juvenile killer whales breathed at a higher rate when travelling (1.6 breaths min-1) compared to resting (1.2) and foraging (1.5)-and that adult males breathed at a higher rate when travelling (1.8) compared to both foraging (1.7) and resting (1.3). The juveniles in our study were estimated to consume 2.5-18.3 L O2 min-1 compared with 14.3-59.8 L O2 min-1 for adult males across all behaviours based on estimates of mass-specific tidal volume and oxygen extraction. Our findings confirm that killer whales take single breaths between dives and indicate that energy expenditure derived from respirations requires using sex, age, and behavioural-specific respiration rates. These findings can be applied to bioenergetics models on a behavioural-specific basis, and contribute towards obtaining better predictions of dive behaviours, energy expenditure and the food requirements of apex predators.

Continuous and bilevel positive airway pressure may improve radiotherapy delivery in patients with intra-thoracic tumors

BackgroundMinimizing tumor motion in radiotherapy for intra-thoracic tumors reduces side-effects by limiting radiation exposure to healthy tissue. Continuous or Bilevel Positive Airway Pressure (CPAP/BiPAP) could achieve this, since it could increase lung inflation and decrease tidal volume variability. We aim to identify the better CPAP/BiPAP setting for minimizing tumor motion. MethodsIn 10 patients (5 with lung cancer, 5 with other intra-thoracic tumors), CPAP/BiPAP was tested with the following settings for 10 min each: CPAP 5, 10 and 15 cmH2O and BiPAP 14/10 cmH2O with a lower (7 breaths/min) and higher back-up respiratory rate (BURR initially 1 breath/min above the spontaneous breathing frequency, with the option to adjust if the patient continued to initiate breaths). Electrical impedance tomography was used to analyse end-expiratory lung impedance (EELI) as an estimate of end-expiratory lung volume and tidal impedance variation (TIV) as an estimate of tidal volume. ResultsNine out of ten patients tolerated all settings; one patient could not sustain CPAP-15. A significant difference in EELI was observed between settings (χ2 22.960, p < 0.001), with most increase during CPAP-15 (median (IQR) 1.03 (1.00 – 1.06), normalized to the EELI during spontaneous breathing). No significant differences in TIV and breathing variability were found between settings. ConclusionsThis study shows that the application of different settings of CPAP/BiPAP in patients with intra-thoracic tumors is feasible and tolerable. BiPAP with a higher BURR may offer the greatest potential for mitigating tumor motion among the applied settings, although further research investigating tumor motion should be conducted.

A non-invasive continuous and real-time volumetric monitoring in spontaneous breathing subjects based on bioimpedance—ExSpiron®Xi: a validation study in healthy volunteers

Tidal volume (TV) monitoring breath-by-breath is not available at bedside in non-intubated patients. However, TV monitoring may be useful to evaluate the work of breathing. A non-invasive device based on bioimpedance provides continuous and real-time volumetric tidal estimation during spontaneous breathing. We performed a prospective study in healthy volunteers aimed at evaluating the accuracy, the precision and the trending ability of measurements of ExSpiron®Xi as compared with the gold standard (i.e. spirometry). Further, we explored whether the differences between the 2 devices would be improved by the calibration of ExSpiron®Xi with a pre-determined tidal volume. Analysis accounted for the repeated nature of measurements within each subject. We enrolled 13 healthy volunteers, including 5 men and 8 women. Tidal volume, TV/ideal body weight (IBW) and respiratory rate (RR) measured with spirometer (TVSpirometer) and with ExSpiron®Xi (TVExSpiron) showed a robust correlation, while minute ventilation (MV) showed a weak correlation, in both non/calibrated and calibrated steps. The analysis of the agreement showed that non-calibrated TVExSpiron underestimated TVspirometer, while in the calibrated steps, TVExSpiron overestimated TVspirometer. The calibration procedure did not reduce the average absolute difference (error) between TVSpirometer and TVExSpiron. This happened similarly for TV/IBW and MV, while RR showed high accuracy and precision. The trending ability was excellent for TV, TV/IBW and RR. The concordance rate (CR) was >95% in both calibrated and non-calibrated measurements. The trending ability of minute ventilation was limited. Absolute error for both calibrated and not calibrated values of TV, TV/IBW and MV accounting for repeated measurements was variably associated with BMI, height and smoking status. Conclusions: Non-invasive TV, TV/IBW and RR estimation by ExSpiron®Xi was strongly correlated with tidal ventilation according to the gold standard spirometer technique. This data was not confirmed for MV. The calibration of the device did not improve its performance. Although the accuracy of ExSpiron®Xi was mild and the precision was limited for TV, TV/IBW and MV, the trending ability of the device was strong specifically for TV, TV/IBW and RR. This makes ExSpiron®Xi a non-invasive monitoring system that may detect real-time tidal volume ventilation changes and then suggest the need to better optimize the patient ventilatory support.

Diaphragm excursions as proxy for tidal volume during spontaneous breathing in invasively ventilated ICU patients

There is a need to monitor tidal volume in critically ill patients with acute respiratory failure, given its relation with adverse clinical outcome. However, quantification of tidal volume in non-intubated patients is challenging. In this proof-of-concept study, we evaluated whether ultrasound measurements of diaphragm excursion could be a valid surrogate for tidal volume in patients with respiratory failure. Diaphragm excursions and tidal volumes were simultaneously measured in invasively ventilated patients (N = 21) and healthy volunteers (N = 20). Linear mixed models were used to estimate the ratio between tidal volume and diaphragm excursion. The tidal volume–diaphragm excursion ratio was 201 mL/cm in ICU patients [95% confidence interval (CI) 161–240 mL/cm], and 361 (294–428) mL/cm in healthy volunteers. An excellent association was shown within participants (R2 = 0.96 in ICU patients, R2 = 0.90 in healthy volunteers). However, the differences between observed tidal volume and tidal volume as predicted by the linear mixed models were considerable: the 95% limits of agreement in Bland–Altman plots were ± 91 mL in ICU patients and ± 396 mL in healthy volunteers. Likewise, the variability in tidal volume estimation between participants was large. This study shows that diaphragm excursions measured with ultrasound correlate with tidal volume, yet quantification of absolute tidal volume from diaphragm excursion is unreliable.

Breathe Easy, an automated respiratory data pipeline for waveform characteristic analysis

Whole body barometric flow through plethysmography is used to study respiratory output in adult rodent genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams for a reasonable estimation of tidal volume and key metabolic parameters. Comprehensive and accurate analysis of respiratory and metabolic data is crucial to modelling congenital, pathogenic, and degenerative diseases. However, such studies produce large amounts of data that are time-consuming and difficult to analyze. The investigator may use cumbersome hand annotation subject to observer bias or a series of software products to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn-key systems limiting options for bespoke respiratory measurement systems. A lack of tools for high-throughput analysis of respiratory datasets remains a major challenge.Here, we present Breathe Easy, a novel open-source pipeline for processing raw recordings and associated metadata into operative outcomes, publication-worthy graphs, and robust statistical analyses including QQ and residual plots for assumption queries and data transformations. This pipeline uses a facile graphical user interface for uploading data files, setting waveform feature thresholds, and defining experimental variables. Behind the scenes we have a fine-tuned, python-based program called BASSPRO that takes the user-settings and selects all quantitatively defined, quality breaths from each period of interest in the signal files. After BASSPRO, STAGG takes those breaths and performs linear mixed effects modeling based on user selections for each dependent variable of interest. Furthermore, our software can integrate non-respiratory experimental outcomes including quantified behavioral or expression data to test for breathing related associations.To validate breath selection and generate optimized default values, results from Breathe Easy were compared to manual selection by experts, which represents the current standard in the field. We show high quality breath selection and a significant reduction in quality of breaths that were rejected by the software. We also demonstrate Breathe Easy’s utility and capacity by examining a 2-year longitudinal study of an Alzheimer’s Disease mouse model to assess contributions of forebrain pathology in disordered breathing (600+ files; 1TB data). We show that, at least with this model of AD, there does not seem to be an association between forebrain pathology and respiratory outcomes.Breathe Easy offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets. NIH: 1F32HL160073-01A1, R01HL130249 44617-S4. BCM McNair Scholar Program, March of Dimes Basil O'Connor Research Award, Parker B. Francis Fellowship, CJ Foundation for SIDS. This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

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.

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 quantifiable 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 respiratory 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 respiratory 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.

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

Evaluating convenient, wearable multi-frequency impedance pneumography (IP)-based respiratory monitoring in ambulatory persons with novel electrode positioning. 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 postures-sitting and supine-and activities-walking and stair-stepping-were evaluated against a simultaneously-obtained spirometer (SP) volume signal. Across all measurements, estimated TV obtained from the patch and thoracic IP maintained a Pearson correlation coefficient (r) of 0.93 ± 0.05 and 0.95 ± 0.05 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. 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. 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.

Estimation of Tidal Volume during Exercise Stress Test from Wearable-Device Measures of Heart Rate and Breathing Rate

Tidal volume (TV), defined as the amount of air that moves in or out of the lungs with each respiratory cycle, is important in evaluating the respiratory function. Although TV can be reliably measured in laboratory settings, this information is hardly obtainable under everyday living conditions. Under such conditions, wearable devices could provide valuable support to monitor vital signs, such as heart rate (HR) and breathing rate (BR). The aim of this study was to develop a model to estimate TV from wearable-device measures of HR and BR during exercise. HR and BR were acquired through the Zephyr Bioharness 3.0 wearable device in nine subjects performing incremental cycling tests. For each subject, TV during exercise was obtained with a metabolic cart (Cosmed). A stepwise regression algorithm was used to create the model using as possible predictors HR, BR, age, and body mass index; the model was then validated using a leave-one-subject-out cross-validation procedure. The performance of the model was evaluated using the explained variance (R2), obtaining values ranging from 0.65 to 0.72. The proposed model is a valid method for TV estimation with wearable devices and can be considered not subject-specific and not instrumentation-specific.

An Automated Respiratory Data Pipeline for Waveform Characteristic Analysis

Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn‐key commercial and custom systems are both used to gather multiple data streams including respiratory‐pressure waveforms, O2 and CO2 measurement, and other data points for a reasonable estimation of tidal volume and key metabolic parameters.These experiments typically produce large amounts of data that are time‐consuming and difficult to analyze and organize. For analysis, the investigator may use cumbersome hand annotation subject to observer bias or several software products may be combined to manage raw data, calculated data, statistical analysis, and graphing. Commercial software, however, is prohibitively expensive or bundled with turn‐key systems that limit options for those using bespoke respiratory measurement systems.To address these deficiencies, we developed a software pipeline for processing raw respiratory recordings and associated metadata (experimental design, age, weight, temperature, etc.) into operative respiratory outcomes, publication‐worthy graphs, and robust statistical analyses. This pipeline consists of three modules. The first module is a Python‐based graphical user interface (GUI) for uploading all relevant data files and allows the user to define and label independent, dependent, and covariate variables relevant to the experimental design, statistical analysis, and graphical outputs. Additionally, parameters can be programmed to define waveform feature segmentation to include manual annotations.In the second module, BASSPRO, the data are processed with a Python program to segment the respiratory waveform based on user‐defined thresholds for movement artifacts and unique features. BASSPRO quantifies respiratory cycle components and demarks unique features including sighs, apneas, expiratory reflexes, and user‐defined entities. The module also allows for browsing the raw signals with markup of automatically annotated breaths to facilitate validation of breath detection and to permit manual annotation of features, if desired.The third module, STAGG, is an R‐based program that accepts the BASSPRO output along with configuration files from the GUI to graph the desired variables while performing a linear mixed effects statistical model analysis of the data with appropriate post‐hoc tests. Following graph generation, statistically significant differences in respiratory variables are automatically annotated in the graphs. Following statistical testing, residual analyses are provided that allow the user to determine if a transformation of their data may be appropriate. If so, transformations can be selected within the GUI so no external data analyses are necessary.The open‐source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high‐throughput studies and machine learning approaches on large data sets.

An Automated Respiratory Data Pipeline for Waveform Characteristic Analysis

Whole body barometric flow through plethysmography is used to study respiratory output in mouse and rat genetic and disease models. Turn-key commercial and custom systems are both used to gather multiple data streams including respiratory-pressure waveforms, O2 and CO2 levels, etc. for a reasonable estimation of tidal volume and key metabolic parameters. These experiments typically produce large amounts of data that are time-consuming and difficult to organize and process. For analysis, the investigator may either choose from proprietary software with limited customization or use cumbersome, manual annotation subject to observer bias. Several software products may be combined to manage raw data manipulation, statistical analyses, and graphing. Commercial software is prohibitively expensive or bundled with turn-key systems, which limits options for those using bespoke respiratory measurement systems. To address these deficiencies, we developed a software pipeline for processing raw respiratory recordings and associated metadata (experimental design, age, weight, temperature, etc.) into operative respiratory outcomes, publication-worthy graphs, and robust statistical analyses. This pipeline consists of three modules. The first module is a Python-based graphical user interface (GUI) for uploading all relevant data files (raw waveforms, metadata, and configuration files) and defining both independent and dependent variables relevant to the experimental design, statistical analysis, and graphical outputs. Additionally, parameters can be programmed to define waveform feature segmentation to include manual annotations. In the second module, Breath-Caller, a Python program segments the respiratory waveform according to user-defined thresholds for movement artifacts and unique features, then quantifies respiratory cycle components and demarks unique features including sighs, apneas, expiratory reflexes, and user-defined entities. The module also allows for browsing the raw signals with markup of automatically annotated breaths to facilitate validation of breath detection and to permit manual annotation of features, if desired. The third module, SG-Runner, is an R-based program that accepts the Breath-Caller output along with configuration files from the GUI to graph the desired variables while performing a linear mixed effects statistical model analysis of the data with appropriate post-hoc tests. SG-Runner is soft-coded to dynamically adjust to the user settings defined in the GUI. Following graph generation, statistically significant differences in respiratory variables are automatically annotated in the graphs. Graphs are exported as individual SVG files for ease of editing and incorporation into figures and as a comprehensive R-markdown file for perusal and review in a web browser. The open-source program offers a facile and highly customizable platform for automated handling and analysis of animal respiratory and metabolic data that sets the stage for high-throughput studies and machine learning approaches on large data sets.

Relative tidal volume and respiratory airflow estimation using tracheal sound and movement during sleep

Airflow is the reference signal to assess sleep respiratory disorders, such as sleep apnea. Previous studies estimated airflow using tracheal sounds in short segments with specific airflow rates, while requiring calibration or a few breaths for tuning the relationship between sound energy and airflow. Airflow-sound relationship can change by posture, sleep stage and airflow rate or tidal volume. We investigated the possibility of estimating surrogates of tidal volume without calibration in the adult sleep apnea population using tracheal sounds and movements. Two surrogates of tidal volume: thoracoabdominal range of sum movement and airflow level were estimated. Linear regression was used to estimate thoracoabdominal range of sum movement from sound energy and the range of movements. The sound energy lower envelope was found to correlate with airflow level. The agreement between reference and estimated signals was assessed by repeated-measure correlation analysis. The estimated tidal volumes were used to estimate the airflow signal. Sixty-one participants (30 females, age: 51±16 years, body mass index: 29.5±6.4 kg m-2 , and apnoea-hypopnea index: 20.2±21.2) were included. Reference and estimated thoracoabdominal range of sum movement of whole night data were significantly correlated with the reference signal extracted from polysomnography (r = 0.5±0.06). Similarly, significant correlations (r = 0.3±0.05) were found for airflow level. Significant differences in estimated surrogates of tidal volume were found between normal breathing and apnea/hypopnea. Surrogate of airflow can be extracted from tracheal sounds and movements, which can be used for assessing the severity of sleep apnea and even phenotyping sleep apnea patients based on the estimated airflow shape.

Home mechanical ventilation: back to basics.

Over recent decades, the use of home mechanical ventilation (HMV) has steadily increased worldwide, with varying prevalence in different countries. The key indication for HMV is chronic respiratory failure with alveolar hypoventilation (e.g., neuromuscular and chest wall disease, obstructive airway diseases, and obesity-related respiratory failure). Most modern home ventilators are pressure-targeted and have sophisticated modes, alarms, and graphics, thereby facilitating optimization of the ventilator settings. However, different ventilators have different algorithms for tidal volume estimation and leak compensation, and there are also several different circuit configurations. Hence, a basic understanding of the fundamentals of HMV is of paramount importance to healthcare workers taking care of patients with HMV. When choosing a home ventilator, they should take into account many factors, including the current condition and prognosis of the primary disease, the patient’s daily performance status, time (hr/day) needed for ventilator support, family support, and financial costs. In this review, to help readers understand the basic concepts of HMV use, we describe the indications for HMV and the factors that influence successful delivery, including interface, circuits, ventilator accessories, and the ventilator itself.

A Gas-Powered, Patient-Responsive Automatic Resuscitator for Use in Acute Respiratory Failure: A Bench and Experimental Study.

During the COVID-19 pandemic, a need for innovative, inexpensive, and simple ventilator devices for mass use has emerged. The Oxylator (CPR Medical Devices, Markham, Ontario, Canada) is an FDA-approved, fist-size, portable ventilation device developed for out-of-hospital emergency ventilation. It has not been tested in conditions of severe lung injury or with added PEEP. We aimed to assess the performance and reliability of the device in simulated and experimental conditions of severe lung injury, and to derive monitoring methods to allow the delivery of safe, individualized ventilation during situations of surge. We bench-tested the functioning of the device with an added PEEP valve extensively, mimicking adult patients with various respiratory mechanics during controlled ventilation, spontaneous breathing, and prolonged unstable conditions where mechanics or breathing effort was changed at every breath. The device was further tested on a porcine model (4 animals) after inducing lung injury, and these results were compared with conventional ventilation modes. The device was stable and predictable, delivering a constant flow (30 L/min) and cycling automatically at the inspiratory pressure set (minimum of 20 cm H2O) above auto-PEEP. Changes in respiratory mechanics manifested as changes in respiratory timing, allowing prediction of tidal volumes from breathing frequency. Simulating lung injury resulted in relatively low tidal volumes (330 mL with compliance of 20 mL/cm H2O). In the porcine model, arterial oxygenation, CO2, and pH were comparable to conventional modes of ventilation. The Oxylator is a simple device that delivered stable ventilation with tidal volumes within a clinically acceptable range in bench and porcine lung models with low compliance. External monitoring of respiratory timing is advisable, allowing tidal volume estimation and recognition of changes in respiratory mechanics. The device can be an efficient, low-cost, and practical rescue solution for providing short-term ventilatory support as a temporary bridge, but it requires a caregiver at the bedside.

Comparison between the Airgo™ Device and a Metabolic Cart during Rest and Exercise.

The aim of this study is to compare the accuracy of Airgo™, a non-invasive wearable device that records breath, with respect to a gold standard. In 21 healthy subjects (10 males, 11 females), four parameters were recorded for four min at rest and in different positions simultaneously by Airgo™ and SensorMedics 2900 metabolic cart. Then, a cardio-pulmonary exercise test was performed using the Erg 800S cycle ergometer in order to test Airgo™’s accuracy during physical effort. The results reveal that the relative error median percentage of respiratory rate was of 0% for all positions at rest and for different exercise intensities, with interquartile ranges between 3.5 (standing position) and 22.4 (low-intensity exercise) breaths per minute. During exercise, normalized amplitude and ventilation relative error medians highlighted the presence of an error proportional to the volume to be estimated. For increasing intensity levels of exercise, Airgo™’s estimate tended to underestimate the values of the gold standard instrument. In conclusion, the Airgo™ device provides good accuracy and precision in the estimate of respiratory rate (especially at rest), an acceptable estimate of tidal volume and minute ventilation at rest and an underestimation for increasing volumes.

Respiratory Volume Monitoring: A Machine-Learning Approach to the Non-Invasive Prediction of Tidal Volume and Minute Ventilation

Continuous monitoring of ventilatory parameters such as tidal volume (TV) and minute ventilation (MV) has shown to be effective in the prevention of respiratory compromise events in hospitalized patients. However, the non-invasive estimation of respiratory volume in non-intubated patients remains an outstanding challenge. In this work, we present a novel approach to respiratory volume monitoring (RVM) that continuously predicts TV and MV in normal subjects. Respiratory flow in 19 volunteers under spontaneous breathing was recorded using respiratory inductance plethysmography and a temperature-based wearable sensor. Temperature signals were processed to identify features such as temperature amplitude and mean value, among others. The feature datasets were then used to train and validate three machine-learning (ML) algorithms for the prediction of respiratory volume based on temperature-related features. A model based on Random-Forest regression resulted in the lowest root mean-square error and was subsequently chosen to predict ventilatory parameters on subject test data not used in the construction of the model. Our predictions achieve a bias (mean error) in TV and MV of 16.04 mL and 0.19 L/min, respectively, which compare well with performance metrics reported in commercially-available RVM systems based on electrical impedance. Our results show that the combination of novel respiratory temperature sensors and machine-learning algorithms can deliver accurate and continuous estimates of TV and MV in healthy subjects.

Wearable respiration monitoring using an in-line few-mode fiber Mach-Zehnder interferometric sensor.

Continuous respiratory monitoring is extensively important in clinical applications. To effectively assess respiration rate (RR), tidal volume (TV), and minute ventilation (MV), we propose and experimentally demonstrate a respiration monitoring system using an in-line few-mode fiber Mach-Zehnder interferometer (FMF-MZI), which is the first to introduce in-line MZI into an optimal wearable design for respiration rate and volume monitoring. The optimal linear region of the proposed sensor is analyzed and positioned by a flexible arch structure with curvature sensitivity up to 8.53 dB/m-1. Respiration monitoring results are in good agreement with a standard spirometer among different individuals. The difference in TV estimation is ± 0.2 L, and the overall error of MV estimation is less than 5%.

Tidal Volume Estimation during Helmet Noninvasive Ventilation: an Experimental Feasibility Study

We performed a bench (BS) and human (HS) study to test the hypothesis that estimation of tidal volume (VT) during noninvasive helmet pressure support ventilation (nHPSV) would be possible using a turbine driven ventilator (TDV) coupled with an intentional leak single-limb vented circuit. During the BS a mannequin was connected to a lung simulator (LS) and at different conditions of respiratory mechanics, positive end expiratory pressure (PEEP) levels and leaks (30, 50 and 80 L/min). All differences were within the 95% limits of agreement (LoA) in all conditions in the Bland-Altman plot. The overall bias (difference between VT measured by TDV and LS) was 35 ml (95% LoA 10 to 57 ml), 15 ml (95% LoA −40 to 70 ml), 141 ml (95% LoA 109 to 173 ml) in the normal, restrictive and obstructive conditions. The bias at different leaks flow in normal condition was 29 ml (95% LoA 19 to 38 ml). In the HS four healthy volunteers using nHPSV had a pneumotachograph (P) inserted through a mouthpiece to measure subject’s VT.The bias showed a scarce clinical relevance. In conclusions, VT estimation seems to be feasible and accurate in all conditions but the obstructive one. Additional leaks seem not to affect VT reliability.

Basic Concepts for Tidal Volume and Leakage Estimation in Non-Invasive Ventilation.

Non-invasive ventilation (NIV) aims to maintain sufficient alveolar ventilation, improve pulmonary gas exchange, assist respiratory muscles, and decrease work of breathing. Monitoring variables such as leaks, tidal volume, and minute ventilation during therapy is crucial to assess the effectiveness of NIV. However, most of the time, leaks and tidal volume are not measured but estimated by NIV devices. Moreover, there are limited data for the accuracy and reliability of these estimations. Herein, we address some technical considerations for tidal volume and leakage estimation during NIV and its impact in clinical practice.