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Physiological monitoring in intensive care units (ICU) generates data thatcan be used in clinical research. However, the recording conditions in clinicalsettings limit the automated extraction of relevant information fromphysiological signals due to noise and artifacts. Therefore, removing artifactsbefore clinical research is essential. Manual annotation by experiencedresearchers, which is the gold standard for removing artifacts, istime-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 aVariational Autoencoder with a statistical detection component for the labelingof artifactual samples to automate the costly process of cleaning physiologicalrecordings. The system is applied to minute-by-minute mean blood pressuresignals from an intensive care unit dataset. Its performance is verified bymanual annotations made by an expert. We benchmark the performance of oursystem with two other systems that combine an ARIMA or an autoencoder-basedmodel with our statistical detection component. Our results indicate that thesystem consistently achieves sensitivity and specificity levels of over 90%.Thus, it provides an initial foundation to automate data cleaning in recordingsfrom ICU.

Hybrid Artifact Detection System for Minute Resolution Blood Pressure Signals from ICU