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

doi:10.1016/j.compbiomed.2008.01.003

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 journal › Article › peer-review

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 language	English (US)
Pages (from-to)	416-424
Number of pages	9
Journal	Computers in Biology and Medicine
Volume	38
Issue number	4
DOIs	https://doi.org/10.1016/j.compbiomed.2008.01.003
State	Published - Apr 2008

Keywords

Acute myocardial ischemia
Artificial neural network
Strain echocardiography

ASJC Scopus subject areas

Computer Science Applications
Health Informatics

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10.1016/j.compbiomed.2008.01.003

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title = "Classification of acute myocardial ischemia by artificial neural network using echocardiographic strain waveforms",

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{\textregistered}, 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.",

keywords = "Acute myocardial ischemia, Artificial neural network, Strain echocardiography",

author = "McMahon, {Eileen M.} and Josef Korinek and Shiro Yoshifuku and Sengupta, {Partho P.} and Armando Manduca and Marek Belohlavek",

note = "Funding Information: We thank Kay Parker and Nancy Peters for assistance during experimental animal studies. We are also grateful to Jennifer Milliken and Danielle Wright for administrative support. This work was supported in part by the HL70363 and HL68555 grants from the National Institutes of Health. ",

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doi = "10.1016/j.compbiomed.2008.01.003",

language = "English (US)",

volume = "38",

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T1 - Classification of acute myocardial ischemia by artificial neural network using echocardiographic strain waveforms

AU - McMahon, Eileen M.

AU - Korinek, Josef

AU - Yoshifuku, Shiro

AU - Sengupta, Partho P.

AU - Manduca, Armando

AU - Belohlavek, Marek

N1 - Funding Information: We thank Kay Parker and Nancy Peters for assistance during experimental animal studies. We are also grateful to Jennifer Milliken and Danielle Wright for administrative support. This work was supported in part by the HL70363 and HL68555 grants from the National Institutes of Health.

PY - 2008/4

Y1 - 2008/4

N2 - 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.

AB - 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.

KW - Acute myocardial ischemia

KW - Artificial neural network

KW - Strain echocardiography

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

Abstract

Keywords

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