Background Inspiratory patient effort under assisted mechanical ventilation is an important quantity for assessing patient–ventilator interaction and recognizing over and under assistance. An established clinical standard is respiratory muscle pressure $$\textit{P}_{\mathrm{mus}}$$ P mus , derived from esophageal pressure ($$\textit{P}_{\mathrm{es}}$$ P es ), which requires the correct placement and calibration of an esophageal balloon catheter. Surface electromyography (sEMG) of the respiratory muscles represents a promising and straightforward alternative technique, enabling non-invasive monitoring of patient activity. Methods A prospective observational study was conducted with patients under assisted mechanical ventilation, who were scheduled for elective bronchoscopy. Airway flow and pressure, esophageal/gastric pressures and sEMG of the diaphragm and intercostal muscles were recorded at four levels of pressure support ventilation. Patient efforts were quantified via the $$\textit{P}_{\mathrm{mus}}$$ P mus -time product ($${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus ), the transdiaphragmatic pressure-time product ($${\mathrm{PTP}}_{\mathrm{di}}$$ PTP di ) and the EMG-time products (ETP) of the two sEMG channels. To improve the signal-to-noise ratio, a method for automatically selecting the more informative of the sEMG channels was investigated. Correlation between ETP and $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus was assessed by determining a neuromechanical conversion factor $$\textit{K}_{\mathrm{EMG}}$$ K EMG between the two quantities. Moreover, it was investigated whether this scalar can be reliably determined from airway pressure during occlusion maneuvers, thus allowing to quantify inspiratory effort based solely on sEMG measurements. Results In total, 62 patients with heterogeneous pulmonary diseases were enrolled in the study, 43 of which were included in the data analysis. The ETP of the two sEMG channels was well correlated with $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus ($$\textit{r}={0.79\pm 0.25}$$ r = 0.79 ± 0.25 and $$\textit{r}={0.84\pm 0.16}$$ r = 0.84 ± 0.16 for diaphragm and intercostal recordings, respectively). The proposed automatic channel selection method improved correlation with $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus ($$\textit{r}={0.87\pm 0.09}$$ r = 0.87 ± 0.09 ). The neuromechanical conversion factor obtained by fitting ETP to $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus varied widely between patients ($$\textit{K}_{\mathrm{EMG}}= {4.32\pm 3.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}/\upmu \hbox {V}}$$ K EMG = 4.32 ± 3.73 cm 2 O / μ V ) and was highly correlated with the scalar determined during occlusions ($$\textit{r}={0.95}$$ r = 0.95 , $$\textit{p}<{.001}$$ p < . 001 ). The occlusion-based method for deriving $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus from ETP showed a breath-wise deviation to $${\mathrm{PTP}}_{\mathrm{mus}}$$ PTP mus of $${0.43\pm 1.73}\,{\hbox {cm}\hbox {H}_{2}\hbox {O}\,\hbox {s}}$$ 0.43 ± 1.73 cm 2 O s across all datasets. Conclusion These results support the use of surface electromyography as a non-invasive alternative for monitoring breath-by-breath inspiratory effort of patients under assisted mechanical ventilation.

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