Anomaly Detection in Intensive Care Unit Data: A Comparative Analysis

International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC), pp. 457–464

Abstract

We present a comparative analysis of anomaly detection (AD) methods applied to vital sign time series data from intensive care units (ICUs). Our study evaluates five unsupervised deep learning-based and three statistical or rule-based AD algorithms with respect to their ability to identify anomalous data points in a binary anomaly classification setting. We have access to physician-annotated datasets of 44 ICU patients. Of these, 38 data sets were used for training and six for evaluation. In addition, selected deep learning models were retrained on a larger dataset comprising 3,000 patients to assess the effect of training set size. Performance is assessed using the F1 score by comparing algorithm outputs with expert annotations. Among all methods, OmniAnomaly and DeepAnT achieved the highest F1 scores, reaching up to 98.38 % and 78.07 %, respectively.

Authors 5

1. Simon Fonck Aachen Embedded Software (Informatik 11)

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Centre for Advanced Simulation and Analytics Healthcare, Forschungszentrum Jülich, 52428 Jülich, Germany

   Embedded Software (Informatik 11), RWTH Aachen University, Im Süsterfeld 9, 52072 Aachen, Germany

2. Lavinia Goldermann Aachen Embedded Software (Informatik 11)

   RWTH Aachen University

   Affiliation as printed

   Embedded Software (Informatik 11), RWTH Aachen University, Im Süsterfeld 9, 52072 Aachen, Germany

3. Sebastian Johannes Fritsch

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Centre for Advanced Simulation and Analytics Healthcare, Forschungszentrum Jülich, 52428 Jülich, Germany

   Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany

4. Lena Olivier

   Forschungszentrum Jülich · Jülich Supercomputing Centre

   Affiliation as printed

   Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, 52428 Jülich, Germany

5. André Stollenwerk Aachen Embedded Software (Informatik 11)

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Centre for Advanced Simulation and Analytics Healthcare, Forschungszentrum Jülich, 52428 Jülich, Germany

   Embedded Software (Informatik 11), RWTH Aachen University, Im Süsterfeld 9, 52072 Aachen, Germany