Comparison of Algorithms for Artifact Detection of Perioperative Blood Pressure Data: A Retrospective Analysis

Abstract

BACKGROUND: Perioperative blood pressure data often contain artifacts that can compromise data integrity for clinical decisions and research. OBJECTIVES: The main objective of this retrospective analysis was to evaluate the efficiency and reliability of various algorithms for artifact detection in perioperative blood pressure data, specifically assessing their performance in different clinical scenarios and measurement methods. METHODS: Data from 106 patients at the Medical University of Vienna were analysed using algorithms based on the interquartile range, Z-Score, Cut-off methods, and Moving Mean/Median. Validation involved comparisons against a reviewer standard set by anaesthesia experts. RESULTS: Using a standard deviation based algorithm was most effective, offering superior accuracy and reliability across scenarios although sensitivity was below 60% for all used algorithms. CONCLUSION: Our results support a scenario-specific approach to artifact detection, underlining the need for research into adaptive algorithms that enhance data quality for clinical and research applications.

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