Abstract
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 non-invasively 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 Pes-derived Pmus. Patient effort was quantified via the Pmus pressure-time-product (PTP). The sEMGderived PTP estimated using the proposed method was highly correlated to Pes-derived PTP (r = 0.95 ± 0.04), and the breath-wise deviation between the two quantities was -0.83 ± 1.73 cmH2Os. Conclusion: The estimated, sEMGderived 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.
| Original language | English |
|---|---|
| Article number | 1 |
| Journal | IEEE Transactions on Biomedical Engineering |
| Volume | 70 |
| Issue number | 1 |
| Pages (from-to) | 247-258 |
| Number of pages | 12 |
| ISSN | 0018-9294 |
| DOIs | |
| Publication status | Published - 01.01.2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 3 Good Health and Well-being
-
SDG 4 Quality Education
-
SDG 9 Industry, Innovation, and Infrastructure
-
SDG 11 Sustainable Cities and Communities
-
SDG 12 Responsible Consumption and Production
-
SDG 14 Life Below Water
-
SDG 15 Life on Land
Fingerprint
Dive into the research topics of 'Model-Based Estimation of Inspiratory Effort Using Surface EMG'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver