  Artifacts are a common problem in physiological time-series data 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 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 (iForest) 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 models based on long
short-term memory (LSTM) and XGBoost. We show that our approach achieves
comparable sensitivity 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.

Machine Learning;Signal Processing