Automated characterization of patient–ventilator interaction using surface electromyography

Abstract

Abstract Background Characterizing patient–ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient. Methods In this study, we recorded surface electromyography ($$\textrm{sEMG}$$ sEMG ) signals from the diaphragm and intercostal muscles and esophageal pressure ($$P_{\textrm{es}}$$ P es ) in mechanically ventilated patients with ARDS. The sEMG recordings were preprocessed, and two different algorithms (triangle algorithm and adaptive thresholding algorithm) were used to automatically detect inspiratory patient effort. Based on the detected inspirations, major asynchronies (ineffective, auto-, and double triggers and double efforts), delayed and synchronous triggers were computationally classified. Reverse triggers were not considered in this study. Subsequently, asynchrony indices were calculated. For the validation of detected efforts, two experts manually annotated inspiratory patient activity in $$P_{\textrm{es}}$$ P es , blinded toward each other, the $$\textrm{sEMG}$$ sEMG signals, and the algorithmic results. We also classified patient–ventilator interaction and calculated asynchrony indices with manually detected inspirations in $$P_{\textrm{es}}$$ P es as a reference for automated asynchrony classification and asynchrony index calculation. Results Spontaneous breathing activity was recognized in 22 out of the 36 patients included in the study. Evaluation of the accuracy of the algorithms using 3057 inspiratory efforts in $$P_{\textrm{es}}$$ P es demonstrated reliable detection performance for both methods. Across all datasets, we found a high sensitivity (triangle algorithm/adaptive thresholding algorithm: 0.93/0.97) and a high positive predictive value (0.94/0.89) against expert annotations in $$P_{\textrm{es}}$$ P es . The average delay of automatically detected inspiratory onset to the $$P_{\textrm{es}}$$ P es reference was $$-$$ - 79 ms/29 ms for the two algorithms. Our findings also indicate that automatic asynchrony index prediction is reliable. For both algorithms, we found the same deviation of $$0.06\pm 0.13$$ 0.06 ± 0.13 to the $$P_{\textrm{es}}$$ P es -based reference. Conclusions Our study demonstrates the feasibility of automating the quantification of patient–ventilator asynchrony in critically ill patients using noninvasive sEMG. This may facilitate more frequent diagnosis of asynchrony and support improving patient–ventilator interaction.