  Although mechanical ventilation is a lifesaving intervention in the ICU, it
has harmful side-effects, such as barotrauma and volutrauma. These harms can
occur due to asynchronies. Asynchronies are defined as a mismatch between the
ventilator timing and patient respiratory effort. Automatic detection of these
asynchronies, and subsequent feedback, would improve lung ventilation and
reduce the probability of lung damage. Neural networks to detect asynchronies
provide a promising new approach but require large annotated data sets, which
are difficult to obtain and require complex monitoring of inspiratory effort.
In this work, we propose a model-based approach to generate a synthetic data
set for machine learning and educational use by extending an existing lung
model with a first-order ventilator model. The physiological nature of the
derived lung model allows adaptation to various disease archetypes, resulting
in a diverse data set. We generated a synthetic data set using 9 different
patient archetypes, which are derived from measurements in the literature. The
model and synthetic data quality have been verified by comparison with clinical
data, review by a clinical expert, and an artificial intelligence model that
was trained on experimental data. The evaluation showed it was possible to
generate patient-ventilator waveforms including asynchronies that have the most
important features of experimental patient-ventilator waveforms.
