Document Detail


Forecasting ICP elevation based on prescient changes of intracranial pressure waveform morphology.
MedLine Citation:
PMID:  20659820     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Interventions of intracranial pressure (ICP) elevation in neurocritical care is currently delivered only after healthcare professionals notice sustained and significant mean ICP elevation. This paper uses the morphological clustering and analysis of ICP (MOCAIP) algorithm to derive 24 metrics characterizing morphology of ICP pulses and test the hypothesis that preintracranial hypertension (Pre-IH) segments of ICP can be differentiated, using these morphological metrics, from control segments that were not associated with any ICP elevation or at least 1 h prior to ICP elevation. Furthermore, we investigate whether a global optimization algorithm could effectively find the optimal subset of these morphological metrics to achieve better classification performance as compared to using full set of MOCAIP metrics. The results showed that Pre-IH segments, using the optimal subset of metrics found by the differential evolution algorithm, can be differentiated from control segments at a specificity of 99% and sensitivity of 37% for these Pre-IH segments 5 min prior to the ICP elevation. While the sensitivity decreased to 21% for Pre-IH segments, 20 min prior to ICP elevation, the high specificity of 99% was retained. The performance using the full set of MOCAIP metrics was shown inferior to results achieved using the optimal subset of metrics. This paper demonstrated that advanced ICP pulse analysis combined with machine learning could potentially leads to the forecasting of ICP elevation so that a proactive ICP management could be realized based on these accurate forecasts.
Authors:
Xiao Hu; Peng Xu; Shadnaz Asgari; Paul Vespa; Marvin Bergsneider
Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural    
Journal Detail:
Title:  IEEE transactions on bio-medical engineering     Volume:  57     ISSN:  1558-2531     ISO Abbreviation:  IEEE Trans Biomed Eng     Publication Date:  2010 May 
Date Detail:
Created Date:  2010-07-27     Completed Date:  2010-12-15     Revised Date:  2014-09-21    
Medline Journal Info:
Nlm Unique ID:  0012737     Medline TA:  IEEE Trans Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1070-8     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Artificial Intelligence*
Computer Simulation
Diagnosis, Computer-Assisted / methods*
Forecasting
Humans
Intracranial Hypertension / diagnosis*,  physiopathology*
Intracranial Pressure*
Manometry / methods*
Models, Neurological
Pattern Recognition, Automated / methods*
Prognosis
Grant Support
ID/Acronym/Agency:
NS054881/NS/NINDS NIH HHS; NS055045/NS/NINDS NIH HHS; NS055998/NS/NINDS NIH HHS; NS059797/NS/NINDS NIH HHS; NS066008/NS/NINDS NIH HHS; R01 NS040122/NS/NINDS NIH HHS; R01 NS040122-03/NS/NINDS NIH HHS; R01 NS066008/NS/NINDS NIH HHS; R01 NS066008-01/NS/NINDS NIH HHS; R21 NS055045/NS/NINDS NIH HHS; R21 NS055045-02/NS/NINDS NIH HHS; R21 NS055998/NS/NINDS NIH HHS; R21 NS055998-02/NS/NINDS NIH HHS; R21 NS059797/NS/NINDS NIH HHS; R21 NS059797-02/NS/NINDS NIH HHS
Comments/Corrections

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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