Model-Based Estimation of Inspiratory Effort Using Surface EMG

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 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.

Analysis and applications of respiratory surface EMG: report of a round table meeting

“…Alternatively, as an estimate of the neural respiratory drive, the area under the inspiratory waveform (i.e., sEMG-time product [ 42 , 48 ]) reflects the intensity of muscle activation. This measure is less sensitive to remaining artifacts than instantaneous sEMG amplitudes.…”

“…Current methods assume a linear relationship between Pmus and sEMG: with k the conversion factor and sEMG the peak amplitude of the signal. This conversion factor, also referred to as the neuromechanical efficiency (NME) index, is determined from simultaneously obtained pneumatic measurements [ 41 , 42 , 48 – 50 ]. This can be done during specific maneuvers, such as end-expiratory occlusions [ 41 , 50 ], in which case a correction factor of 0.7 or 0.8 is needed as the diaphragm is more efficient during isometric contractions as compared to tidal breathing [ 41 , 42 ].…”

“…This can be done during specific maneuvers, such as end-expiratory occlusions [ 41 , 50 ], in which case a correction factor of 0.7 or 0.8 is needed as the diaphragm is more efficient during isometric contractions as compared to tidal breathing [ 41 , 42 ]. Newer model-based approaches use the equation of motion to determine the conversion factor during tidal breathing and computationally derive Pmus [ 48 ]. The latter study [ 48 ], demonstrated high reliability and accuracy for estimation of Pmus, even for recordings with low sEMG quality, but this should be confirmed in an external cohort of critically ill patients.…”

“…Newer model-based approaches use the equation of motion to determine the conversion factor during tidal breathing and computationally derive Pmus [ 48 ]. The latter study [ 48 ], demonstrated high reliability and accuracy for estimation of Pmus, even for recordings with low sEMG quality, but this should be confirmed in an external cohort of critically ill patients.…”

Mechanical ventilation intervention based on machine learning from vital signs monitoring: a scoping review

“…The classification of MV parameters waveforms related to PVA does not correspond to the clinical and pathophysiological mechanisms [3]. In such a case, the PVA is more sensitive to be detected by monitoring the esophageal pressures and the diaphragm's electrical activity [9]. As a result, multimodal monitoring (including vital signs monitoring) of the conditions leading to the PVA during MV support is required to improve early warning or detection of an increased frequency of asynchronies that alert the operator.…”