  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.
