Time Series Mining Approach for Noninvasive Intracranial Pressure Assessment: An Investigation of Different Regularization Techniques

A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function f...

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Bibliographic Details
Published in2009 WRI World Congress on Computer Science and Information Engineering Vol. 5; pp. 382 - 386
Main Authors Shaozhi Wu, Peng Xu, Asgari, S., Bergsneider, M., Xiao Hu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2009
Subjects
Arterial blood pressure
Biomedical engineering
Biomedical measurements
Computer science
Cranial pressure
Data engineering
Data mining
Feature extraction
Laboratories
Least square
Mapping function
Non-invasive ICP estimation
Singular value decomposition
Spatial databases
Standard Tikhonov regularization
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Summary:A data mining framework has been proposed to estimate intracranial pressure (ICP) non-invasively in our previous work. In the corresponding approach, the feature vector extracted from arterial blood pressure (ABP) and flow velocity (FV) is translated to the estimated errors by the mapping function for each entry in the database. In this paper, three different mapping function solutions, linear least squares (LLS), truncated singular value decomposition (TSVD) and standard Tikhonov regularization (STR) are systemically tested to compare the possible effects of different solutions on the non-invasive ICP estimation. The conducted comparison demonstrated that the selection of mapping function solution actually influences the estimation. Among the tested three solutions for mapping function, TSVD and STR show better ICP estimation performance with smaller ICP errors than LLS.
ISBN:9780769535074
0769535070
DOI:10.1109/CSIE.2009.861