  Physiological monitoring in intensive care units (ICU) generates data that
can be used in clinical research. However, the recording conditions in clinical
settings limit the automated extraction of relevant information from
physiological signals due to noise and artifacts. Therefore, removing artifacts
before clinical research is essential. Manual annotation by experienced
researchers, which is the gold standard for removing artifacts, is
time-consuming and costly due to the volume of the data generated in the ICU.
In this study, we propose a hybrid artifact detection system that combines a
Variational Autoencoder with a statistical detection component for the labeling
of artifactual samples to automate the costly process of cleaning physiological
recordings. The system is applied to minute-by-minute mean blood pressure
signals from an intensive care unit dataset. Its performance is verified by
manual annotations made by an expert. We benchmark the performance of our
system with two other systems that combine an ARIMA or an autoencoder-based
model with our statistical detection component. Our results indicate that the
system consistently achieves sensitivity and specificity levels of over 90%.
Thus, it provides an initial foundation to automate data cleaning in recordings
from ICU.
