Model-based Estimation of Inspiratory Effort using Surface EMG

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 (<italic>P</italic>_mus) derived from esophageal pressure (<italic>P</italic>_es) measurements. As yet, no reliable non-invasive and unobtrusive alternatives exist to continuously quantify <italic>P</italic>_mus. Methods: We propose a model-based approach to estimate <italic>P</italic>_mus 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 <italic>P</italic>_mus 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 <italic>P</italic>_es-derived <italic>P</italic>_mus. Patient effort was quantified via the <italic>P</italic>_mus pressure-time-product (PTP). The sEMG-derived PTP estimated using the proposed method was highly correlated to <italic>P</italic>_es-derived PTP (<inline-formula><tex-math notation="LaTeX">$\mathit{r}=\text{0.95}\pm \text{0.04}$</tex-math></inline-formula>), and the breath-wise deviation between the two quantities was <inline-formula><tex-math notation="LaTeX">$-\text{0.83}\pm \text{1.73}\,\text{cmH}_\text{2}\text{O}\,\text{s}$</tex-math></inline-formula>. Conclusion: The estimated, sEMG-derived <italic>P</italic>_mus is closely related to the <italic>P</italic>_es-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.