Wiki
A Curated Knowledge Base for Advanced Neuromonitoring
Find clear explanations of how signals, metrics, and analytics are interpreted in practice.
Wiki Knowledge Base
A Practical Guide for Data Interpretation in Neurocritical Care
This knowledge base brings together structured explanations of key physiologic signals and commonly used derived metrics in neuromonitoring and neurocritical care, along with the methods behind them. It focuses on how data is generated, what influences signal quality, and how interpretation can change depending on context.
You’ll find clear breakdowns of core concepts, common artifacts, and analytical approaches used in continuous monitoring, along with practical considerations for how these measures are applied and where they can be misleading.
Use the link below to explore the full knowledge base.
Access a curated knowledge base for physiologic signals & analytics
Understand key concepts in physiologic data interpretation
Learn how data is structured and interpreted
Reference standardized explanations for signal analytics
FAQs
A Wiki is a type of structured website used to organize and continuously update information in a way that is easy to navigate, edit, and expand over time. It is commonly used in technical and scientific fields to maintain shared knowledge that can be refined as understanding improves.
In this context, it functions as a curated knowledge base focused specifically on neuromonitoring. It brings together explanations of physiologic signals, derived analytics, and interpretation considerations into a single, structured reference that can grow alongside the field.
Advanced analytics only create value when users share a common understanding of:
- what the data represents
- when it can be trusted
- and when it cannot
This resource exists to build that shared understanding, openly, rigorously, and with humility about uncertainty.
It is used as a reference for interpreting continuous multimodal data and derived analytics in context. This includes understanding core signals such as ICP, ABP, and EEG, recognizing artifacts and data quality issues, and learning how commonly used metrics (such as PRx and CPPopt) are computed and applied.
The goal is not just to describe these methods, but to support more consistent and careful interpretation before they are used in research or clinical workflows.
This resource is intended for clinicians, researchers, engineers, and trainees working with neuromonitoring data or interpreting physiologic signals in critical care settings.
It is especially relevant for users involved in EEG, ICP, ABP, or multimodal monitoring who need to understand how derived metrics behave and where interpretation challenges may arise.
The resource focuses on interpretation and understanding, including:
- Core signals (e.g., ICP, ABP, EEG)
- Common artefacts and data quality issues in the ICU
- Derived analytics and algorithms (e.g., PRx, CPPopt, aEEG, DSA)
- How signal quality and artefacts affect downstream metrics
- Clinical workflows and retrospective review pitfalls
- Limitations, uncertainty, and appropriate use of analytics
These concepts are intended to support clearer and more consistent interpretation across different analytical contexts.
Content is grounded in published literature, real-world experience, and multimodal monitoring practice.
You can use this resource as a reference when reviewing or working with continuous physiologic data. It is designed to help you look up specific signals, metrics, or concepts, understand how they are defined, and interpret them more confidently in context. Many users refer to it while analyzing data, validating assumptions, or clarifying how certain values are derived and what limitations may apply.
You can navigate through topics to explore related concepts, compare different approaches, and build a more complete understanding of how these elements connect in practice. It is also useful for onboarding, training, or deepening familiarity with less commonly used metrics.
If you’d like to engage further, you can suggest improvements or share ideas through GitHub, or join discussions and ask questions in the Discord community. These channels support collaboration and help connect this reference with real-world use and feedback.
This resource is part of a broader neuromonitoring analytics ecosystem, where each component serves a distinct role.
- The Wiki focuses on interpretation and understanding, helping users make sense of physiologic signals, derived analytics, and their limitations
- GitHub provides the tools, code, and reproducible implementations used to process and analyze that data
- Discord supports discussion, questions, and real-world edge cases from clinicians, researchers, and engineers
Together, these resources create a continuous loop between shared understanding, practical implementation, and ongoing discussion.
No. These tools are designed to be system-agnostic and are not dependent on any specific platform or infrastructure.
They are intended to work across a variety of systems and data environments, making them broadly applicable to both research and clinical workflows regardless of the underlying monitoring setup.