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      Intracranial Pressure Monitoring Signals After Traumatic Brain Injury: A Narrative Overview and Conceptual Data Science Framework

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          Abstract

          Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

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          Most cited references86

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          Approximate entropy as a measure of system complexity.

          Techniques to determine changing system complexity from data are evaluated. Convergence of a frequently used correlation dimension algorithm to a finite value does not necessarily imply an underlying deterministic model or chaos. Analysis of a recently developed family of formulas and statistics, approximate entropy (ApEn), suggests that ApEn can classify complex systems, given at least 1000 data values in diverse settings that include both deterministic chaotic and stochastic processes. The capability to discern changing complexity from such a relatively small amount of data holds promise for applications of ApEn in a variety of contexts.
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            The enigma of Mayer waves: Facts and models.

            Mayer waves are oscillations of arterial pressure occurring spontaneously in conscious subjects at a frequency lower than respiration (approximately 0.1 Hz in humans). Mayer waves are tightly coupled with synchronous oscillations of efferent sympathetic nervous activity and are almost invariably enhanced during states of sympathetic activation. For this reason, the amplitude of these oscillations has been proposed as a surrogate measure of sympathetic activity, although in the absence of a clear knowledge of their underlying physiology. Some studies have suggested that Mayer waves result from the activity of an endogenous oscillator located either in the brainstem or in the spinal cord. Other studies, mainly based on the effects of sinoaortic baroreceptor denervation, have challenged this view. Several models of dynamic arterial pressure control have been developed to predict Mayer waves. In these models, it was anticipated that the numerous dynamic components and fixed time delays present in the baroreflex loop would result in the production of a resonant, self-sustained oscillation of arterial pressure. Recent analysis of the various transfer functions of the rat baroreceptor reflex suggests that Mayer waves are transient oscillatory responses to hemodynamic perturbations rather than true feedback oscillations. Within this frame, the amplitude of Mayer waves would be determined both by the strength of the triggering perturbations and the sensitivity of the sympathetic component of the baroreceptor reflex.
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              Continuous monitoring of cerebrovascular pressure reactivity allows determination of optimal cerebral perfusion pressure in patients with traumatic brain injury.

              To define optimal cerebral perfusion pressure (CPPOPT) in individual head-injured patients using continuous monitoring of cerebrovascular pressure reactivity. To test the hypothesis that patients with poor outcome were managed at a cerebral perfusion pressure (CPP) differing more from their CPPOPT than were patients with good outcome. Retrospective analysis of prospectively collected data. Neurosciences critical care unit of a university hospital. A total of 114 head-injured patients admitted between January 1997 and August 2000 with continuous monitoring of mean arterial blood pressure (MAP) and intracranial pressure (ICP). MAP, ICP, and CPP were continuously recorded and a pressure reactivity index (PRx) was calculated online. PRx is the moving correlation coefficient recorded over 4-min periods between averaged values (6-sec periods) of MAP and ICP representing cerebrovascular pressure reactivity. When cerebrovascular reactivity is intact, PRx has negative or zero values, otherwise PRx is positive. Outcome was assessed at 6 months using the Glasgow Outcome Scale. A total of 13,633 hrs of data were recorded. CPPOPT was defined as the CPP where PRx reaches its minimum value when plotted against CPP. Identification of CPPOPT was possible in 68 patients (60%). In 22 patients (27%), CPPOPT was not found because it presumably lay outside the studied range of CPP. Patients' outcome correlated with the difference between CPP and CPPOPT for patients who were managed on average below CPPOPT (r =.53, p <.001) and for patients whose mean CPP was above CPPOPT (r = -.40, p <.05). CPPOPT could be identified in a majority of patients. Patients with a mean CPP close to CPPOPT were more likely to have a favorable outcome than those whose mean CPP was more different from CPPOPT. We propose use of the criterion of minimal achievable PRx to guide future trials of CPP oriented treatment in head injured patients.
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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                28 August 2020
                2020
                : 11
                : 959
                Affiliations
                [1] 1Department of Mechanical and Materials Engineering, College of Engineering and Applied Sciences , Cincinnati, OH, United States
                [2] 2NSF I/UCRC Center for Intelligent Maintenance Systems , Cincinnati, OH, United States
                [3] 3Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, University of Cincinnati Gardner Neuroscience Institute , Cincinnati, OH, United States
                Author notes

                Edited by: Mattias K. Sköld, Uppsala University, Sweden

                Reviewed by: Marek Czosnyka, University of Cambridge, United Kingdom; Danilo Cardim, University of Texas Southwestern Medical Center, United States; Celeste Dias, University of Porto, Portugal

                *Correspondence: Brandon Foreman foremabo@ 123456ucmail.uc.edu

                This article was submitted to Neurotrauma, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2020.00959
                7496370
                33013638
                faf7e8a9-9cf5-4e52-837b-6fc3ca78b1e5
                Copyright © 2020 Dai, Jia, Pahren, Lee and Foreman.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 October 2019
                : 24 July 2020
                Page count
                Figures: 7, Tables: 3, Equations: 4, References: 93, Pages: 16, Words: 11947
                Funding
                Funded by: National Institute of Neurological Disorders and Stroke 10.13039/100000065
                Categories
                Neurology
                Review

                Neurology
                data science,intracranial pressure,traumatic brain injury,machine learning,prognostics and health maintenance

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