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  • Tabassian, MahdiKatholieke University of Leuven, Belgium; University of Bologna, Italy; Lab Cardiovasc Imaging and Dynam, Belgium (författare)

Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification

  • Artikel/kapitelEngelska2017

Förlag, utgivningsår, omfång ...

  • 2017-03-20
  • SPRINGER,2017
  • printrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:liu-139539
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-139539URI
  • https://doi.org/10.1007/s10554-017-1108-0DOI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA). In order to describe the spatial relations between the segmental traces, the PCA-derived temporal features of all LV segments were concatenated. The obtained set of features was then used to build an automatic classification system. The proposed methodology was applied to a group of 60 MRI-delayed enhancement confirmed infarct patients and 60 controls in order to detect myocardial infarction. An average classification accuracy of 87% with corresponding sensitivity and specificity rates of 89% and 85%, respectively was obtained by the proposed methodology applied on the strain rate curves. This classification performance was better than that obtained with the same methodology applied on the strain curves, reading of two expert cardiologists as well as comparative classification systems using only the spatial distribution of the end-systolic strain and peak-systolic strain rate values. This study shows the potential of machine learning in the field of cardiac deformation imaging where an efficient representation of the spatio-temporal characteristics of the segmental deformation curves allowed automatic classification of infarcted from control hearts with high accuracy.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Alessandrini, MartinoKatholieke University of Leuven, Belgium; University of Bologna, Italy (författare)
  • Herbots, LievenKatholieke University of Leuven, Belgium (författare)
  • Mirea, OanaKatholieke University of Leuven, Belgium (författare)
  • Pagourelias, Efstathios D.Katholieke University of Leuven, Belgium (författare)
  • Jasaityte, RutaKatholieke University of Leuven, Belgium (författare)
  • Engvall, JanLinköpings universitet,Avdelningen för kardiovaskulär medicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Fysiologiska kliniken US(Swepub:liu)janen74 (författare)
  • De Marchi, LucaUniversity of Bologna, Italy (författare)
  • Masetti, GuidoUniversity of Bologna, Italy (författare)
  • Dhooge, JanKatholieke University of Leuven, Belgium (författare)
  • Katholieke University of Leuven, Belgium; University of Bologna, Italy; Lab Cardiovasc Imaging and Dynam, BelgiumKatholieke University of Leuven, Belgium; University of Bologna, Italy (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:The International Journal of Cardiovascular Imaging: SPRINGER33:8, s. 1159-11671569-57941875-83121573-0743

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