Artifact identification and removal methodologies for intracranial pressure signals: a systematic scoping review.

Abstract

Objective. Intracranial pressure measurement (ICP) is an essential component of deriving of multivariate data metrics foundational to improving understanding of high temporal relationships in cerebral physiology. A significant barrier to this work is artifact ridden data. As such, the objective of this review was to examine the existing literature pertinent to ICP artifact management.Methods.A search of five databases (BIOSIS, SCOPUS, EMBASE, PubMed, and Cochrane Library) was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines with the PRISMA Extension for Scoping Review. The search question examined the methods for artifact management for ICP signals measured in human/animals.Results.The search yielded 5875 unique results. There were 19 articles included in this review based on inclusion/exclusion criteria and article references. Each method presented was categorized as: (1) valid ICP pulse detection algorithms and (2) ICP artifact identification and removal methods. Machine learning-based and filter-based methods indicated the best results for artifact management; however, it was not possible to elucidate a single most robust method.Conclusion.There is a significant lack of standardization in the metrics of effectiveness in artifact removal which makes comparison difficult across studies. Differences in artifacts observed on patient neuropathological health and recording methodologies have not been thoroughly examined and introduce additional uncertainty regarding effectiveness.Significance. This work provides critical insights into existing literature pertaining to ICP artifact management as it highlights holes in the literature that need to be adequately addressed in the establishment of robust artifact management methodologies.