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

Frontiers in Neurology

卷:11

Brandon Foreman²  Jay Lee³  Xiaodong Jia³  Honghao Dai³  Laura Pahren³ 

[1] Department of Mechanical and Materials Engineering, College of Engineering and Applied Sciences, Cincinnati, OH, United States;

[2] Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, University of Cincinnati Gardner Neuroscience Institute, Cincinnati, OH, United States;

[3] NSF I/UCRC Center for Intelligent Maintenance Systems, Cincinnati, OH, United States;

关键词: data science;    intracranial pressure;    traumatic brain injury;    machine learning;    prognostics and health maintenance;   

DOI  :  10.3389/fneur.2020.00959

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown