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

TL;DR

This paper introduces a hybrid artifact detection system for minute-resolution blood pressure signals in ICU, combining a Variational Autoencoder and statistical detection to automate data cleaning with high accuracy.

Contribution

The study presents a novel hybrid system that automates artifact detection in ICU blood pressure signals, outperforming existing models in sensitivity and specificity.

Findings

Achieves over 90% sensitivity and specificity.

Outperforms ARIMA and autoencoder-based benchmarks.

Provides a foundation for automated ICU data cleaning.

Abstract

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.