Bergmann Tobias, Vakitbilir Nuray, Gomez Alwyn, Islam Abrar, Stein Kevin Y, Sainbhi Amanjyot Singh, Silvaggio Noah, Marquez Izzy, Froese Logan, Zeiler Frederick A
Department of Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, Canada.
Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Canada.
Physiol Meas. 2024 Dec 17;45(12). doi: 10.1088/1361-6579/ad9af4.
. 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.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.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.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.. 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.
颅内压测量（ICP）是获取多变量数据指标的重要组成部分，这些指标对于增进对脑生理学中高时间分辨率关系的理解至关重要。这项工作的一个重大障碍是充满伪迹的数据。因此，本综述的目的是研究与ICP伪迹管理相关的现有文献。根据系统评价和Meta分析的首选报告项目（PRISMA）指南以及范围综述的PRISMA扩展版，对五个数据库（BIOSIS、SCOPUS、EMBASE、PubMed和Cochrane图书馆）进行了检索。检索问题考察了在人/动物中测量的ICP信号的伪迹管理方法。检索产生了5875个独特的结果。根据纳入/排除标准和文章参考文献，本综述纳入了19篇文章。所介绍的每种方法都被归类为：（1）有效的ICP脉冲检测算法和（2）ICP伪迹识别与去除方法。基于机器学习和基于滤波器的方法在伪迹管理方面显示出最佳效果；然而，无法阐明一种最强大的单一方法。在伪迹去除有效性指标方面存在严重缺乏标准化的情况，这使得跨研究比较变得困难。在患者神经病理学健康状况和记录方法上观察到的伪迹差异尚未得到充分研究，这给有效性带来了额外的不确定性。这项工作为与ICP伪迹管理相关的现有文献提供了关键见解，因为它突出了文献中的漏洞，这些漏洞需要在建立强大的伪迹管理方法时得到充分解决。
