Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.

<h4>Introduction</h4>The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natu...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Marius Reto Bigler, Christian Seiler
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/535c46b521e84a38874ae6ea4fcff047
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:535c46b521e84a38874ae6ea4fcff047
record_format	dspace
spelling	oai:doaj.org-article:535c46b521e84a38874ae6ea4fcff0472021-12-02T20:10:42ZDetection of myocardial ischemia by intracoronary ECG using convolutional neural networks.1932-620310.1371/journal.pone.0253200https://doaj.org/article/535c46b521e84a38874ae6ea4fcff0472021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253200https://doaj.org/toc/1932-6203<h4>Introduction</h4>The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG).<h4>Method</h4>This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia.<h4>Results</h4>Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection.<h4>Conclusions</h4>When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.Marius Reto BiglerChristian SeilerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253200 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marius Reto Bigler Christian Seiler Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
description	<h4>Introduction</h4>The electrocardiogram (ECG) is a valuable tool for the diagnosis of myocardial ischemia as it presents distinctive ischemic patterns. Deep learning methods such as convolutional neural networks (CNN) are employed to extract data-derived features and to recognize natural patterns. Hence, CNN enable an unbiased view on well-known clinical phenomenon, e.g., myocardial ischemia. This study tested a novel, hypothesis-generating approach using pre-trained CNN to determine the optimal ischemic parameter as obtained from the highly susceptible intracoronary ECG (icECG).<h4>Method</h4>This was a retrospective observational study in 228 patients with chronic coronary syndrome. Each patient had participated in clinical trials with icECG recording and ST-segment shift measurement at the beginning (i.e., non-ischemic) and the end (i.e., ischemic) of a one-minute proximal coronary artery balloon occlusion establishing the reference. Using these data (893 icECGs in total), two pre-trained, open-access CNN (GoogLeNet/ResNet101) were trained to recognize ischemia. The best performing CNN during training were compared with the icECG ST-segment shift for diagnostic accuracy in the detection of artificially induced myocardial ischemia.<h4>Results</h4>Using coronary patency or occlusion as reference for absent or present myocardial ischemia, receiver-operating-characteristics (ROC)-analysis of manually obtained icECG ST-segment shift (mV) showed an area under the ROC-curve (AUC) of 0.903±0.043 (p<0.0001, sensitivity 80%, specificity 92% at a cut-off of 0.279mV). The best performing CNN showed an AUC of 0.924 (sensitivity 93%, specificity 92%). DeLong-Test of the ROC-curves showed no significant difference between the AUCs. The underlying morphology responsible for the network prediction differed between the trained networks but was focused on the ST-segment and the T-wave for myocardial ischemia detection.<h4>Conclusions</h4>When tested in an experimental setting with artificially induced coronary artery occlusion, quantitative icECG ST-segment shift and CNN using pathophysiologic prediction criteria detect myocardial ischemia with similarly high accuracy.
format	article
author	Marius Reto Bigler Christian Seiler
author_facet	Marius Reto Bigler Christian Seiler
author_sort	Marius Reto Bigler
title	Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
title_short	Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
title_full	Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
title_fullStr	Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
title_full_unstemmed	Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.
title_sort	detection of myocardial ischemia by intracoronary ecg using convolutional neural networks.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/535c46b521e84a38874ae6ea4fcff047
work_keys_str_mv	AT mariusretobigler detectionofmyocardialischemiabyintracoronaryecgusingconvolutionalneuralnetworks AT christianseiler detectionofmyocardialischemiabyintracoronaryecgusingconvolutionalneuralnetworks
_version_	1718374945454555136

Detection of myocardial ischemia by intracoronary ECG using convolutional neural networks.

Ejemplares similares