Paper Info
Title
Noninvasive intracranial pressure assessment based on a data-mining approach using a nonlinear mapping function.
Abstract
The current gold standard to determine intracranial pressure (ICP) involves an invasive procedure for direct access to the intracranial compartment. The risks associated with this invasive procedure include intracerebral hemorrhage, infection, and discomfort. We previously proposed an innovative data-mining framework of noninvasive ICP (NICP) assessment. The performance of the proposed framework relies on designing a good mapping function. We attempt to achieve performance gain by adopting various linear and nonlinear mapping functions. Our results demonstrate that a nonlinear mapping function based on the kernel spectral regression technique significantly improves the performance of the proposed data-mining framework for NICP assessment in comparison to other linear mapping functions.
Year
DOI
Venue
2012
10.1109/TBME.2010.2093897
IEEE Trans. Biomed. Engineering
Keywords
Field
DocType
quadratic programming (qp),nicp,ordinary least squares (ols),blood pressure measurement,recursive weighted least squares (rwl),regression analysis,noninvasive icp (nicp),spectral analysis,kernel spectral regression (ksr),nonlinear functions,kernel spectral regression,brain,noninvasive intracranial pressure assessment,data mining,nonlinear mapping function,linear mapping function,medical diagnostic computing,gold standard,ordinary least square,nonlinear dynamics,kernel,time series analysis,quadratic program,hemodynamics,feature extraction
Kernel (linear algebra),Time series,Data mining,Nonlinear system,Invasive Procedure,Regression analysis,Computer science,Intracranial pressure,Feature extraction,Linear map
Journal
Volume
Issue
ISSN
59
3
1558-2531
Citations 
PageRank 
References 
2
0.42
4
Authors
6
Name
Order
Citations
PageRank
Sunghan Kim1164.26
Fabien Scalzo26815.42
Marvin Bergsneider36710.75
Paul Vespa492.09
Neil Martin5111.46
Xiao Hu67213.64