Implementation of Artifact Detection in Critical Care: A Methodological Review

TL;DR

This review analyzes over 80 artifact detection algorithms in critical care, highlighting their limitations and proposing standardized interfaces to enhance reusability, validation, and real-time clinical integration.

Contribution

It introduces a taxonomy for AD algorithms and provides recommendations for standardizing data interfaces to improve clinical applicability.

Findings

Most algorithms are specific to one CCU type

Limited validation across different OEM monitors

Few algorithms operate in real-time or are implemented clinically

Abstract

Artifact Detection (AD) techniques minimize the impact of artifacts on physiologic data acquired in Critical Care Units (CCU) by assessing quality of data prior to Clinical Event Detection (CED) and Parameter Derivation (PD). This methodological review introduces unique taxonomies to synthesize over 80 AD algorithms based on these six themes: (1) CCU; (2) Physiologic Data Source; (3) Harvested data; (4) Data Analysis; (5) Clinical Evaluation; and (6) Clinical Implementation. Review results show that most published algorithms: (a) are designed for one specific type of CCU; (b) are validated on data harvested only from one Original Equipment Manufacturer (OEM) monitor; (c) generate Signal Quality Indicators (SQI) that are not yet formalised for useful integration in clinical workflows; (d) operate either in standalone mode or coupled with CED or PD applications; (e) are rarely evaluated in real-time; and (f) are not implemented in clinical practice. In conclusion, it is recommended that AD algorithms conform to generic input and output interfaces with commonly defined data: (1) type; (2) frequency; (3) length; and (4) SQIs. This shall promote (a) reusability of algorithms across different CCU domains; (b) evaluation on different OEM monitor data; (c) fair comparison through formalised SQIs; (d) meaningful integration with other AD, CED and PD algorithms; and (e) real-time implementation in clinical workflows.