ArticleAnnals of intensive care2024
Automated characterization of patient-ventilator interaction using surface electromyography.
Article in Annals of intensive care, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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3 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Application progress of machine learning in patient-ventilator asynchrony during mechanical ventilation: a systematic review.Critical care (London, England) · 2025Pooled it
- Automatic Detection of Onset and Offset of Respiratory Electromyographic Activity in Severe COPD Patients on Non-Invasive Mechanical Ventilation.IEEE journal of translational engineering in health and medicine · 2026Article
- Respiratory muscle dysfunction in acute and chronic respiratory failure: how to diagnose and how to treat?European respiratory review : an official journal of the European Respiratory Society · 2024Review
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundCharacterizing patient-ventilator interaction in critically ill patients is time-consuming and requires trained staff to evaluate the behavior of the ventilated patient.
methodsIn this study, we recorded surface electromyography ([Formula: see text]) signals from the diaphragm and intercostal muscles and esophageal pressure ([Formula: see text]) 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 [Formula: see text], blinded toward each other, the [Formula: see text] signals, and the algorithmic results. We also classified patient-ventilator interaction and calculated asynchrony indices with manually detected inspirations in [Formula: see text] as a reference for automated asynchrony classification and asynchrony index calculation.
resultsSpontaneous 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 [Formula: see text] 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 [Formula: see text]. The average delay of automatically detected inspiratory onset to the [Formula: see text] reference was [Formula: see text]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 [Formula: see text] to the [Formula: see text]-based reference.
conclusionsOur 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.
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