Detection of patient-ventilator asynchrony based on esophageal pressure using a convolutional neural network
I. Ihaddouchen (TU Delft - Mechanical Engineering)
D.M.J. Tax – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
A. Schoe – Mentor (Leiden University Medical Center)
E. de Jonge – Graduation committee member (Leiden University Medical Center)
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Abstract
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.