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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2021-12-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12072 |
| _version_ | 1797427043709747200 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T08:39:40Z |
| format | Article |
| id | doaj.art-275c066929b14e28914f82fda80d3b4c |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T08:39:40Z |
| publishDate | 2021-12-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-275c066929b14e28914f82fda80d3b4c2023-12-02T17:15:38ZengHindawi-IETIET Signal Processing1751-96751751-96832021-12-0115967468510.1049/sil2.12072Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogramNastaran Jafari Hafshejani0Alireza Mehridehnavi1Reza Hajian2Shabnam Boudagh3Mohaddeseh Behjati4Department of Biomedical Engineering School of Advanced Medical Technology Isfahan University of Medical Sciences Isfahan IranDepartment of Biomedical Engineering School of Advanced Medical Technology Isfahan University of Medical Sciences Isfahan IranDepartment of Biomedical Engineering Amirkabir University of Technology Tehran IranRajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran IranRajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran IranAbstract 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.https://doi.org/10.1049/sil2.12072bloodcardiologydiseaseselectrocardiographyfeature extractionmedical signal detection |
| spellingShingle | Nastaran Jafari Hafshejani Alireza Mehridehnavi Reza Hajian Shabnam Boudagh Mohaddeseh Behjati Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram IET Signal Processing blood cardiology diseases electrocardiography feature extraction medical signal detection |
| title | 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_short | Identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram |
| title_sort | identification of myocardial infarction using morphological features of electrocardiogram and vectorcardiogram |
| topic | blood cardiology diseases electrocardiography feature extraction medical signal detection |
| url | https://doi.org/10.1049/sil2.12072 |
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