Detection of patient-ventilator asynchrony based on esophageal pressure using a convolutional neural network

Detection of patient-ventilator asynchrony based on esophageal pressure using a convolutional neural network

Ihaddouchen, Imane (TU Delft Mechanical, Maritime and Materials Engineering)

Tax, D.M.J. (mentor)
Schoe, A. (mentor)
de Jonge, E. (graduation committee)

Delft University of Technology
Erasmus Universiteit Rotterdam
Leiden University Medical Center

Technical Medicine | Sensing and Stimulation

2023-03-20

Introduction: In intensive care units (ICU), the most significant life support technology for patients with acute respiratory failure is mechanical ventilation. A mismatch between ventilatory support and patient demand is referred to as patient-ventilator asynchrony (PVA), and it is associated with a series of adverse clinical outcomes. Although the use of a reference signal for patient effort is critical in recognition of PVA, existing detection algorithms are frequently solely based on the ventilator’s airway pressure (Paw) and flow-time signals. The aim of this study was to develop an automated detection algorithm for PVA using the ventilator’s Paw, flow-time and esophageal pressure (Pes) signals.

Methods: We proposed a two-dimensional convolutional neural network (2DCNN) to detect two types of PVA (reverse triggering (RT) and premature cycling) using a dataset of respiratory cycles recorded from 11 patients. Mechanical ventilation experts with access to the Pes signal annotated 12.337 respiratory cycles to create a gold standard dataset. Several techniques for a potential class imbalance problem, as well as several changes to the initial model architecture, were investigated. A leave-one-patient-out cross-validation technique was used to evaluate model performance. The proposed Pes-based 2DCNN (Pes_2DCNN) was compared to a similar model based solely on the ventilator Paw and flow-time signals (PF_2DCNN).

Results: The proposed Pes_2DCNN exhibited superior performance in detecting RT as compared to PF_2DCNN in terms of area under the receiver operating characteristic (AUROC) (0.80 ± 0.07 vs. 0.75 ± 0.13, respectively; p < 0.01). Furthermore, the results indicate that the class imbalance solutions did not improve the performance for detection of RT. For detection of premature cycling, Pes_2DCNN also outperformed PF_2DCNN in terms of AUROC (0.88 ± 0.09 vs. 0.71 ± 0.24, respectively; p < 0.01).

Conclusion: The findings of this study suggest the added value of the Pes signal in detection of RT and premature cycling. However, because this is a preliminary study, more research is required to further investigate the importance of the Pes signal in PVA detection.

Intensive Care
Mechanical ventilation
Patient-ventilator asynchrony
Machine Learning
Convolutional Neural Network

http://resolver.tudelft.nl/uuid:07b1650c-a8e4-4db6-baad-e516fd0a5dd0

Student theses

master thesis

© 2023 Imane Ihaddouchen