Surface EMG-based quantification of inspiratory effort: a quantitative comparison with Pes

Abstract

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.

Model-Based Estimation of Inspiratory Effort Using Surface EMG

“…These processing steps were performed on all available channels, resulting in one sEMG envelope signal for each channel. We have previously shown that respiratory sEMG channels often do not carry the same amount of information and that a channel selection method, i.e., using the channel with higher signal-to-noise ratio (SNR), can lead to a higher correlation to P mus [16]. Therefore, we employ our previously described automatic selection technique and only use the EMG channel with the highest SNR for the EMG-derived measure of inspiratory effort.…”

“…Therefore, we employ our previously described automatic selection technique and only use the EMG channel with the highest SNR for the EMG-derived measure of inspiratory effort. To this end, for each channel, the SNR was approximated by forming the ratio between the maximum EMG amplitudes reached during tidal breathing and the level of the measurement noise in between patient activities (both noise and signal power were estimated across full recordings), see [16] for further details. The selected envelope with higher estimated SNR is denoted by EMG sel (t).…”

“…It was previously proposed to convert between electrophysiological signals (e.g., EAdi or sEMG) and muscle pressure (e.g., P di or P mus ) using a linear neuromechanical coupling index, defined as K EMG = ∆P mus /∆EMG (or K EMG = ∆P di /∆EMG) and measured in cmH 2 O/µV, cf. [13], [15], [16]. Beck et al [12] showed that the assumption of linearity between EAdi and P di is valid over a wide range of physiological activities and different levels of pressure support.…”

“…This is particularly useful in patients who exhibit a predominantly thoracic breathing pattern. Similar to EAdi, recent studies [15], [16] have demonstrated an occlusion-based technique for estimating P mus from sEMG. A patient-specific neuromechanical coupling index was determined during occlusions and an approximate P mus signal was derived by scaling the respiratory sEMG channels.…”

“…The estimation employs a sensor fusion approach, which uses both the EMG data and pneumatic measurements over the full course of the ventilation to form a P mus estimate that is only weakly affected by noise in the individual signals; see [29] for a comprehensive overview of sensor fusion methods. The performance of the proposed method is evaluated by comparison against the invasive P es -based measurement, and compared to the performance of the previously proposed occlusion method [16]. One of the main advantages of a model-based approach lies in the improvement of signal quality when all measurements are combined, thus, the novel method attains a better estimation accuracy than the occlusion-based method.…”