  Artifacts are a common problem in physiological time series collected from
intensive care units (ICU) and other settings. They affect the quality and
reliability of clinical research and patient care. Manual annotation of
artifacts is costly and time-consuming, rendering it impractical. Automated
methods are desired. Here, we propose a novel fully unsupervised approach to
detect artifacts in clinical-standard, minute-by-minute resolution ICU data
without any prior labeling or signal-specific knowledge. Our approach combines
a variational autoencoder (VAE) and an isolation forest (IF) into a hybrid
model to learn features and identify anomalies in different types of vital
signs, such as blood pressure, heart rate, and intracranial pressure. We
evaluate our approach on a real-world ICU dataset and compare it with
supervised benchmark models based on long short-term memory (LSTM) and XGBoost
and statistical methods such as ARIMA. We show that our unsupervised approach
achieves comparable sensitivity to fully supervised methods and generalizes
well to an external dataset. We also visualize the latent space learned by the
VAE and demonstrate its ability to disentangle clean and noisy samples. Our
approach offers a promising solution for cleaning ICU data in clinical research
and practice without the need for any labels whatsoever.
