Implementation of visual data mining for unsteady blood flow field in an aortic aneurysm

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

This study was performed to determine the relations between the features of wall shear stress and aneurysm rupture. For this purpose, visual data mining was performed in unsteady blood flow simulation data for an aortic aneurysm. The time-series data of wall shear stress given at each grid point were converted to spatial and temporal indices, and the grid points were sorted using a self-organizing map based on the similarity of these indices. Next, the results of cluster analysis were mapped onto the real space of the aortic aneurysm to specify the regions that may lead to aneurysm rupture. With reference to previous reports regarding aneurysm rupture, the visual data mining suggested specific hemodynamic features that cause aneurysm rupture.

Original languageEnglish
Pages (from-to)393-398
Number of pages6
JournalJournal of Visualization
Volume14
Issue number4
DOIs
Publication statusPublished - 2011 Dec

Keywords

  • Aortic aneurysm
  • Self-organizing map
  • Visual data mining
  • Wall shear stress

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electrical and Electronic Engineering

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