Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

Abstract Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12‐lead electrocardiogram (ECG) waves incl...

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Autores principales:	Nastaran Jafari Hafshejani, Alireza Mehridehnavi, Reza Hajian, Shabnam Boudagh, Mohaddeseh Behjati
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	classification and regression tree (CART) electrocardiogram (ECG) feedforward neural network (FFNN) morphological features myocardial infarction (MI) vectorcardiogram (VCG) Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/275c066929b14e28914f82fda80d3b4c
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spelling	oai:doaj.org-article:275c066929b14e28914f82fda80d3b4c2021-11-09T10:16:48ZIdentification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram1751-96831751-967510.1049/sil2.12072https://doaj.org/article/275c066929b14e28914f82fda80d3b4c2021-12-01T00:00:00Zhttps://doi.org/10.1049/sil2.12072https://doaj.org/toc/1751-9675https://doaj.org/toc/1751-9683Abstract Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12‐lead electrocardiogram (ECG) waves including T‐wave, Q‐wave, and ST‐segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch‐Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior‐MI and inferior‐MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation.Nastaran Jafari HafshejaniAlireza MehridehnaviReza HajianShabnam BoudaghMohaddeseh BehjatiWileyarticleclassification and regression tree (CART)electrocardiogram (ECG)feedforward neural network (FFNN)morphological featuresmyocardial infarction (MI)vectorcardiogram (VCG)TelecommunicationTK5101-6720ENIET Signal Processing, Vol 15, Iss 9, Pp 674-685 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	classification and regression tree (CART) electrocardiogram (ECG) feedforward neural network (FFNN) morphological features myocardial infarction (MI) vectorcardiogram (VCG) Telecommunication TK5101-6720
spellingShingle	classification and regression tree (CART) electrocardiogram (ECG) feedforward neural network (FFNN) morphological features myocardial infarction (MI) vectorcardiogram (VCG) Telecommunication TK5101-6720 Nastaran Jafari Hafshejani Alireza Mehridehnavi Reza Hajian Shabnam Boudagh Mohaddeseh Behjati Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
description	Abstract Cardiac failure, such as myocardial infarction (MI), is one of the most serious causes of mortality worldwide. MI is the sign of cardiac cell damage as a result of decreased blood oxygen level, which causes some morphological changes in the form of 12‐lead electrocardiogram (ECG) waves including T‐wave, Q‐wave, and ST‐segment. The main goal of this study is to represent vectorcardiography (VCG) as a complementary diagnostic tool of the ECG method to discriminate the various type of MI from normal cases. The system proposed in this study was analysed on the Physikalisch‐Technische Bundesanstalt diagnostic ECG database and a recorded signal database for 80 MI and 52 healthy cases. Each record consists of 15 ECG and VCG signals. In this study, tridimensional morphological features were applied to the classification and regression tree (CART) and the feedforward neural network classifier. To classify MI cases from healthy control cases of our recorded database, classification and regression tree achieved the same results when VCG features or ECG features were applied with an accuracy of 99.4%, a sensitivity of 100%, and a specificity of 98.7%. Further, by using VCG Octant features with this current method, anterior‐MI and inferior‐MI were separated with an accuracy of 98.9%, a sensitivity of 98%, and a specificity of 100%. The outcomes prove that the VCG features performed more accurately than ECG features in MI localisation.
format	article
author	Nastaran Jafari Hafshejani Alireza Mehridehnavi Reza Hajian Shabnam Boudagh Mohaddeseh Behjati
author_facet	Nastaran Jafari Hafshejani Alireza Mehridehnavi Reza Hajian Shabnam Boudagh Mohaddeseh Behjati
author_sort	Nastaran Jafari Hafshejani
title	Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
title_short	Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
title_full	Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
title_fullStr	Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
title_full_unstemmed	Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
title_sort	identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/275c066929b14e28914f82fda80d3b4c
work_keys_str_mv	AT nastaranjafarihafshejani identificationofmyocardialinfarctionusingmorphologicalfeaturesofelectrocardiogramandvectorcardiogram AT alirezamehridehnavi identificationofmyocardialinfarctionusingmorphologicalfeaturesofelectrocardiogramandvectorcardiogram AT rezahajian identificationofmyocardialinfarctionusingmorphologicalfeaturesofelectrocardiogramandvectorcardiogram AT shabnamboudagh identificationofmyocardialinfarctionusingmorphologicalfeaturesofelectrocardiogramandvectorcardiogram AT mohaddesehbehjati identificationofmyocardialinfarctionusingmorphologicalfeaturesofelectrocardiogramandvectorcardiogram
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Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

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