Classification of acute myocardial ischemia by artificial neural network using echocardiographic strain waveforms

Eileen M. McMahon, Josef Korinek, Shiro Yoshifuku, Partho P. Sengupta, Armando Manduca, Marek Belohlavek

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

Echocardiographic strain waveforms are highly variable, so their interpretation is experience-dependent and subjective. We tested whether an artificial neural network (ANN) can distinguish between strain waveforms obtained at baseline and during experimentally induced acute ischemia. An open-chest model of coronary occlusion and acute ischemia was used in 14 adult pigs. Strain waveforms were obtained using a GE Vivid 7 ultrasound system. An ANN design was implemented in MATLAB®, and backpropagation and "leave-one-out" processes were used to train and test it. Specificity of 86% and sensitivity of 87% suggest that ANNs could aid in diagnostic prescreening of echocardiographic strain waveforms.

Original languageEnglish (US)
Pages (from-to)416-424
Number of pages9
JournalComputers in Biology and Medicine
Volume38
Issue number4
DOIs
StatePublished - Apr 1 2008

Keywords

  • Acute myocardial ischemia
  • Artificial neural network
  • Strain echocardiography

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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