Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram

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‐w...

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Bibliographic Details
Published in:IET signal processing 2021-12, Vol.15 (9), p.674-685
Main Authors: Hafshejani, Nastaran Jafari, Mehridehnavi, Alireza, Hajian, Reza, Boudagh, Shabnam, Behjati, Mohaddeseh
Format: Article
Language:English
Subjects:
blood
cardiology
classification and regression tree (CART)
diseases
Electrocardiogram
electrocardiogram (ECG)
Electrocardiography
feature extraction
feedforward neural network (FFNN)
Health aspects
Heart attack
medical signal detection
morphological features
Mortality
myocardial infarction (MI)
Neural networks
vectorcardiogram (VCG)
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Summary: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.
ISSN:1751-9675
1751-9683
DOI:10.1049/sil2.12072