Automated characterization of patient-ventilator interaction using surface electromyography.

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 ([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

SUBMITTER: Sauer J 

PROVIDER: S-EPMC10897101 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature