6,077 research outputs found
A mathematical model for breath gas analysis of volatile organic compounds with special emphasis on acetone
Recommended standardized procedures for determining exhaled lower respiratory
nitric oxide and nasal nitric oxide have been developed by task forces of the
European Respiratory Society and the American Thoracic Society. These
recommendations have paved the way for the measurement of nitric oxide to
become a diagnostic tool for specific clinical applications. It would be
desirable to develop similar guidelines for the sampling of other trace gases
in exhaled breath, especially volatile organic compounds (VOCs) which reflect
ongoing metabolism. The concentrations of water-soluble, blood-borne substances
in exhaled breath are influenced by: (i) breathing patterns affecting gas
exchange in the conducting airways; (ii) the concentrations in the
tracheo-bronchial lining fluid; (iii) the alveolar and systemic concentrations
of the compound. The classical Farhi equation takes only the alveolar
concentrations into account. Real-time measurements of acetone in end-tidal
breath under an ergometer challenge show characteristics which cannot be
explained within the Farhi setting. Here we develop a compartment model that
reliably captures these profiles and is capable of relating breath to the
systemic concentrations of acetone. By comparison with experimental data it is
inferred that the major part of variability in breath acetone concentrations
(e.g., in response to moderate exercise or altered breathing patterns) can be
attributed to airway gas exchange, with minimal changes of the underlying blood
and tissue concentrations. Moreover, it is deduced that measured end-tidal
breath concentrations of acetone determined during resting conditions and free
breathing will be rather poor indicators for endogenous levels. Particularly,
the current formulation includes the classical Farhi and the Scheid series
inhomogeneity model as special limiting cases.Comment: 38 page
Theoretical open-loop model of respiratory mechanics in the extremely preterm infant
Non-invasive ventilation is increasingly used for respiratory support in
preterm infants, and is associated with a lower risk of chronic lung disease.
However, this mode is often not successful in the extremely preterm infant in
part due to their markedly increased chest wall compliance that does not
provide enough structure against which the forces of inhalation can generate
sufficient pressure. To address the continued challenge of studying treatments
in this fragile population, we developed a nonlinear lumped-parameter model of
respiratory system mechanics of the extremely preterm infant that incorporates
nonlinear lung and chest wall compliances and lung volume parameters tuned to
this population. In particular we developed a novel empirical representation of
progressive volume loss based on compensatory alveolar pressure increase
resulting from collapsed alveoli. The model demonstrates increased rate of
volume loss related to high chest wall compliance, and simulates laryngeal
braking for elevation of end-expiratory lung volume and constant positive
airway pressure (CPAP). The model predicts that low chest wall compliance
(chest stiffening) in addition to laryngeal braking and CPAP enhance breathing
and delay lung volume loss. These results motivate future data collection
strategies and investigation into treatments for chest wall stiffening.Comment: 22 pages, 5 figure
Modeling of heart rate variability and respiratory muscle activity in organophosphate poisoned patients
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksWe propose an extended model of cardiovascular regulation to assess heart rate variability in patients poisoned with organophosphate during their treatment with mechanical ventilation. The model was modified to fit a population of 21 patients poisoned with organophosphorus compounds and undergoing mechanical ventilation. The extended model incorporated the respiratory muscle activity measured by surface electromyography for quantifying the vagal-sympathetic engagement during spontaneous breathing test. The order and structure of the parasympathetic and the sympathetic transfer function with respect to the original model were modified to a second-order system. In this extended model, the parameters related to the vagal-sympathetic response (corner frequency and constant gain) were correlated with respiratory muscle activity. When the diaphragm's contractions were stronger, the sympathetic corner frequency increased while the parasympathetic corner frequency and gain decreased. Thus, the proposed model could be useful to improve the ventilatory support and pharmacological treatment for patients poisoned with organophosphorus compounds considering the vagal-sympathetic response inferred from the respiratory muscle activityPeer ReviewedPostprint (author's final draft
Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation
Patient-ventilator asynchronies can be detected by close monitoring of ventilator screens by clinicians or through automated algorithms. However, detecting complex patient-ventilator interactions (CP-VI), consisting of changes in the respiratory rate and/or clusters of asynchronies, is a challenge. Sample Entropy (SE) of airway flow (SE-Flow) and airway pressure (SE-Paw) waveforms obtained from 27 critically ill patients was used to develop and validate an automated algorithm for detecting CP-VI. The algorithm’s performance was compared versus the gold standard (the ventilator’s waveform recordings for CP-VI were scored visually by three experts; Fleiss’ kappa = 0.90 (0.87–0.93)). A repeated holdout cross-validation procedure using the Matthews correlation coefficient (MCC) as a measure of effectiveness was used for optimization of different combinations of SE settings (embedding dimension, m, and tolerance value, r), derived SE features (mean and maximum values), and the thresholds of change (Th) from patient’s own baseline SE value. The most accurate results were obtained using the maximum values of SE-Flow (m = 2, r = 0.2, Th = 25%) and SE-Paw (m = 4, r = 0.2, Th = 30%) which report MCCs of 0.85 (0.78–0.86) and 0.78 (0.78–0.85), and accuracies of 0.93 (0.89–0.93) and 0.89 (0.89–0.93), respectively. This approach promises an improvement in the accurate detection of CP-VI, and future study of their clinical implications.This work was funded by projects PI16/01606, integrated in the Plan Nacional de R+D+I and co-funded by the ISCIII- Subdirección General de Evaluación y el Fondo Europeo de Desarrollo Regional (FEDER). RTC-2017-6193-1 (AEI/FEDER UE). CIBER Enfermedades Respiratorias, and Fundació Parc Taulí
An Improved Dynamic Model for the Respiratory Response to Exercise
ABSTRACT: Respiratory system modeling has been extensively studied in steady-state conditions to simulate sleep disorders, to predict its behavior under ventilatory diseases or stimuli and to simulate its interaction with mechanical ventilation. Nevertheless, the studies focused on the instantaneous response are limited, which restricts its application in clinical practice. The aim of this study is double: firstly, to analyze both dynamic and
static responses of two known respiratory models under exercise stimuli by using an incremental exercise stimulus sequence (to analyze the model responses when step inputs are applied) and experimental data (to assess prediction capability of each model).
Secondly, to propose changes in the models’ structures to improve their transient and stationary responses. The versatility of the resulting model vs. the other two is shown according to the ability to simulate ventilatory stimuli, like exercise, with a proper regulation of the arterial blood gases, suitable constant times and a better adjustment to experimental data. The proposed model adjusts the breathing pattern every respiratory cycle using an optimization criterion based on minimization of work of breathing through regulation of respiratory frequency
Accelerated Cardiac Diffusion Tensor Imaging Using Joint Low-Rank and Sparsity Constraints
Objective: The purpose of this manuscript is to accelerate cardiac diffusion
tensor imaging (CDTI) by integrating low-rankness and compressed sensing.
Methods: Diffusion-weighted images exhibit both transform sparsity and
low-rankness. These properties can jointly be exploited to accelerate CDTI,
especially when a phase map is applied to correct for the phase inconsistency
across diffusion directions, thereby enhancing low-rankness. The proposed
method is evaluated both ex vivo and in vivo, and is compared to methods using
either a low-rank or sparsity constraint alone. Results: Compared to using a
low-rank or sparsity constraint alone, the proposed method preserves more
accurate helix angle features, the transmural continuum across the myocardium
wall, and mean diffusivity at higher acceleration, while yielding significantly
lower bias and higher intraclass correlation coefficient. Conclusion:
Low-rankness and compressed sensing together facilitate acceleration for both
ex vivo and in vivo CDTI, improving reconstruction accuracy compared to
employing either constraint alone. Significance: Compared to previous methods
for accelerating CDTI, the proposed method has the potential to reach higher
acceleration while preserving myofiber architecture features which may allow
more spatial coverage, higher spatial resolution and shorter temporal footprint
in the future.Comment: 11 pages, 16 figures, published on IEEE Transactions on Biomedical
Engineerin
A novel strategy to fit and validate physiological models: a case study of acardiorespiratory model for simulation of incremental aerobic exercise
Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness.Peer ReviewedPostprint (published version
Isoprene and acetone concentration profiles during exercise on an ergometer
A real-time recording setup combining exhaled breath VOC measurements by
proton transfer reaction mass spectrometry (PTR-MS) with hemodynamic and
respiratory data is presented. Continuous automatic sampling of exhaled breath
is implemented on the basis of measured respiratory flow: a flow-controlled
shutter mechanism guarantees that only end-tidal exhalation segments are drawn
into the mass spectrometer for analysis.
Exhaled breath concentration profiles of two prototypic compounds, isoprene
and acetone, during several exercise regimes were acquired, reaffirming and
complementing earlier experimental findings regarding the dynamic response of
these compounds reported by Senthilmohan et al. [1] and Karl et al. [2]. While
isoprene tends to react very sensitively to changes in pulmonary ventilation
and perfusion due to its lipophilic behavior and low Henry constant,
hydrophilic acetone shows a rather stable behavior. Characteristic (median)
values for breath isoprene concentration and molar flow, i.e., the amount of
isoprene exhaled per minute are 100 ppb and 29 nmol/min, respectively, with
some intra-individual day-to-day variation. At the onset of exercise breath
isoprene concentration increases drastically, usually by a factor of ~3-4
within about one minute. Due to a simultaneous increase in ventilation, the
associated rise in molar flow is even more pronounced, leading to a ratio
between peak molar flow and molar flow at rest of ~11.
Our setup holds great potential in capturing continuous dynamics of
non-polar, low-soluble VOCs over a wide measurement range with simultaneous
appraisal of decisive physiological factors affecting exhalation kinetics.Comment: 35 page
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