A polynomial model of patient-specific breathing effort during controlled mechanical ventilation

Redmond, Daniel P.; Docherty, Paul; Chiew, Yeong Shiong; Chase, J. Geoffrey

doi:10.1109/embc.2015.7319402

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…Due to the lack of effort modeling, this method typically performs poorly 32 . Redmond et al 35 considered a quadratic function to model effort. Moreover, their method may determine the initial and final instants of the breath, as our formulation does.…”

Section: Discussionmentioning

confidence: 99%

Mixed‐integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Victor

Máximo

Matsumoto

et al. 2022

Numer Methods Biomed Eng

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Information about respiratory mechanics such as resistance, elastance, and muscular pressure is important to mitigate ventilator-induced lung injury.Particularly during pressure support ventilation, the available options to quantify breathing effort and calculate respiratory system mechanics are often invasive or complex. We herein propose a robust and flexible estimation of respiratory effort better than current methods. We developed a method for non-invasively estimating breathing effort using only flow and pressure signals. Mixed-integer quadratic programming (MIQP) was employed, and the binary variables were the switching moments of the respiratory effort waveform. Mathematical constraints, based on ventilation physiology, were set for some variables to restrict feasible solutions. Simulated and patient data were used to verify our method, and the results were compared to an established estimation methodology. Our

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Section: Discussionmentioning

confidence: 99%

Mixed‐integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Victor

Máximo

Matsumoto

et al. 2022

Numer Methods Biomed Eng

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“…Several active lung models have been proposed in past studies for the simulation of mechanical ventilation considering the patient's spontaneous breathing [10], [12]- [18]. Jodat, Ronald W. et al conducted a comprehensive analysis of the respiratory system and obtained the mutual mechanical relationship among the various parts of the respiratory system [13].…”

Section: With the Development Of Medical Technology Mechanicalmentioning

confidence: 99%

“…In PSV mode, the operating pressure of the ventilator works as a piecewise function, as shown in equation (18). This equation is obtained by interpolation fitting the experimental curve based on the working principle of the active lung simulator ASL5000.…”

Section: Experiments and Mathematical Simulation Of Mechanical Venmentioning

confidence: 99%

Dynamic Characteristics of a Mechanical Ventilation System With Spontaneous Breathing

et al. 2019

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Mechanical ventilation is an important and effective method for the treatment of pulmonary diseases patients with spontaneous breathing. Spontaneous breathing refers to the physiological breathing activity caused by the respiratory muscle. These patients retain some ability to breathe spontaneously, but do not reach the level of normal breathing. Mathematical simulation and modeling of the mechanical ventilation system are crucial for research on mechanical ventilation. In this paper, a novel pneumatic model of a mechanical ventilation system considering patients' spontaneous breathing is presented. Mathematical equations are accurately derived to explain the principles of the respiratory system and mechanical ventilation system. An experimental prototype is designed to confirm the correctness and validity of the pneumatic model. The goodness of fit shows that the mathematical simulation curve fits well with the experimental curve, thus confirming the accuracy of the pneumatic model. For patients with a certain degree of spontaneous breathing, the mechanical ventilation mode is set to the pressure support ventilation (PSV) mode, and variations in the flow, pressure and tidal volume curves are observed by changing specific respiratory mechanics parameters such as the compliance (C), the effective area of the throttle in the pneumatic model (A), and the muscle pressure difference (P mus). From the results, it can be concluded that the resistance of the mechanical ventilation system can be equivalent to A. The dynamic characteristics (mainly flow characteristics, tidal volume characteristics and pressure characteristics) of the mechanical ventilation system are directly influenced by variations in C, A and P mus. This study is an important reference for setting ventilation levels and ventilator control parameters. The results of this research are valuable for the diagnosis and treatment of respiratory diseases.

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“…An alternative approach for achieving identifiability during spontaneous breathing uses physiological knowledge about the shape of respiratory efforts to constrain the estimated P mus waveform. This was demonstrated by incorporating a P mus template waveform or physiologically motivated constraints into the parameter estimation [22], [26], [27]. These approaches have so far only been demonstrated on small patient cohorts and it is still unclear if the assumptions about the shape of P mus will hold in real ventilation scenarios.…”

Section: Introductionmentioning

confidence: 99%

Model-Based Estimation of Inspiratory Effort Using Surface EMG

Grahoff

Petersen

Walterspacher

et al. 2023

IEEE Trans. Biomed. Eng.

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Objective: The quantification of inspiratory patient effort in assisted mechanical ventilation is essential for the adjustment of ventilatory assistance and for assessing patient-ventilator interaction. The inspiratory effort is usually measured via the respiratory muscle pressure (Pmus) derived from esophageal pressure (Pes) measurements. As yet, no reliable non-invasive and unobtrusive alternatives exist to continuously quantify Pmus. Methods: We propose a model-based approach to estimate Pmus noninvasively during assisted ventilation using surface electromyographic (sEMG) measurements. The method combines the sEMG and ventilator signals to determine the lung elastance and resistance as well as the neuromechanical coupling of the respiratory muscles via a novel regression technique. Using the equation of motion, an estimate for Pmus can then be calculated directly from the lung mechanical parameters and the pneumatic ventilator signals. Results: The method was applied to data recorded from a total of 43 ventilated patients and validated against Pesderived Pmus. Patient effort was quantified via the Pmus pressure-time-product (PTP). The sEMG-derived PTP estimated using the proposed method was highly correlated to Pes-derived PTP (r = 0.95 ± 0.04), and the breathwise deviation between the two quantities was −0.83 ± 1.73 cmH 2 O s. Conclusion: The estimated, sEMG-derived Pmus is closely related to the Pes-based reference and allows to reliably quantify inspiratory effort. Significance: The proposed technique provides a valuable tool for physicians to assess patients undergoing assisted mechanical ventilation and, thus, may support clinical decision making.

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A polynomial model of patient-specific breathing effort during controlled mechanical ventilation

Cited by 8 publications

References 13 publications

Mixed‐integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Mixed‐integer quadratic programming approach for noninvasive estimation of respiratory effort profile during pressure support ventilation

Dynamic Characteristics of a Mechanical Ventilation System With Spontaneous Breathing

Model-Based Estimation of Inspiratory Effort Using Surface EMG

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