Surface EMG-based Estimation of Breathing Effort for Neurally Adjusted Ventilation Control

Petersen, Eike; Graßhoff, Jan; Eger, Marcus; Rostalski, Philipp

doi:10.1016/j.ifacol.2020.12.654

Cited by 7 publications

(9 citation statements)

References 24 publications

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“…It was already suggested by several authors that ordinary least squares (OLS) regression for respiratory parameter estimation appears to be brittle: e.g., [33] reported a strong sensitivity of OLS to small time delays in the pneumatic signals. Several authors [21], [28], [34] discussed potential shortcomings of the sample-wise OLS regression approach and presented several cases were it failed to identify good model parameters for the data. Additional results given in appendix II confirm the potential shortcomings of using the direct OLS-based regression approach.…”

Section: Discussionmentioning

confidence: 99%

“…2) Parameter and Effort Estimation: The determination of 'good' parameters for equation (3) proves to be a difficult task, and the naïve solution using ordinary least squares (OLS) regression on all measured data points during spontaneous breathing appears to be brittle [21], [28], [33], [34]. In fact, the samplewise OLS solution in some cases fails to capture the 'big picture' of the ventilation data and tends to overfit samples that a user would deem less important, such as uninformative time phases, identical repetitive breaths, or artifacts.…”

Section: Model-based Effort Estimationmentioning

confidence: 99%

“…In this work, we pursue a novel model-based approach that incorporates respiratory sEMG signals as additional measurements to gain further information about P mus . We have recently proposed a concept for neurally adjusted ventilation based on incorporating electromyographic measurements in [28]-here, we aim to substantially extend this technique and validate it on a large cohort of 43 patients. The herein proposed method allows to non-invasively estimate the lung parameters and inspiratory effort by combining ventilator signals with respiratory sEMG during assisted breathing.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

Section: Model-based Effort Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model-Based Estimation of Inspiratory Effort Using Surface EMG

Grahoff

Petersen

Walterspacher

et al. 2023

IEEE Trans. Biomed. Eng.

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“…Because the relation between the measured signals is nonunique, prior knowledge enables obtaining sensible estimates of the desired parameters and the patient effort. In literature, extra sensing ([9]- [11], [13]), maneuvers ([14]- [17]), or stringent assumptions on the shape of the effort ( [14], [15], [23]) are used to solve this challenge. However, from the earlier defined estimation setting, it is clear that this is practically undesired.…”

Section: Estimation Goalmentioning

confidence: 99%

“…Therefore, it is not suitable for many patients. In [13], non-invasive surface electromyography (EMG) measurements are used to estimate the patient's breathing effort. The presented methods all require additional, possibly invasive, sensing.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive Breathing Effort Estimation of Mechanically Ventilated Patients Using Sparse Optimization

Reinders

Hunnekens²,

Wouw

et al. 2022

IEEE Open J. Control. Syst.

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Mechanical ventilators facilitate breathing for patients who cannot breathe (sufficiently) on their own. The aim of this paper is to estimate relevant lung parameters and the spontaneous breathing effort of a ventilated patient that help keeping track of the patient's clinical condition. A key challenge is that estimation using the available sensors for typical model structures results in a non-identifiable parametrization. A sparse optimization algorithm to estimate the lung parameters and the patient effort, without interfering with the patient's treatment, using an 1 -regularization approach is presented. It is confirmed that accurate estimates of the lung parameters and the patient effort can be retrieved through a simulation case study and an experimental case study.

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