Computer Modeling of the Heart for ECG Interpretation—A Review
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Hearts |
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| Online Access: | https://www.mdpi.com/2673-3846/2/3/28 |
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| author | Olaf Dössel Giorgio Luongo Claudia Nagel Axel Loewe |
| author_facet | Olaf Dössel Giorgio Luongo Claudia Nagel Axel Loewe |
| author_sort | Olaf Dössel |
| collection | DOAJ |
| description | Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning. |
| first_indexed | 2024-03-10T07:38:00Z |
| format | Article |
| id | doaj.art-aeca9846961c428c8ef5c621ff272ccb |
| institution | Directory Open Access Journal |
| issn | 2673-3846 |
| language | English |
| last_indexed | 2024-03-10T07:38:00Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Hearts |
| spelling | doaj.art-aeca9846961c428c8ef5c621ff272ccb2023-11-22T13:19:10ZengMDPI AGHearts2673-38462021-07-012335036810.3390/hearts2030028Computer Modeling of the Heart for ECG Interpretation—A ReviewOlaf Dössel0Giorgio Luongo1Claudia Nagel2Axel Loewe3Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyInstitute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyInstitute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyInstitute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), 76131 Karlsruhe, GermanyComputer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.https://www.mdpi.com/2673-3846/2/3/28in silicoelectrophysiologyelectrocardiogramECGcardiac diseasearrhythmia |
| spellingShingle | Olaf Dössel Giorgio Luongo Claudia Nagel Axel Loewe Computer Modeling of the Heart for ECG Interpretation—A Review Hearts in silico electrophysiology electrocardiogram ECG cardiac disease arrhythmia |
| title | Computer Modeling of the Heart for ECG Interpretation—A Review |
| title_full | Computer Modeling of the Heart for ECG Interpretation—A Review |
| title_fullStr | Computer Modeling of the Heart for ECG Interpretation—A Review |
| title_full_unstemmed | Computer Modeling of the Heart for ECG Interpretation—A Review |
| title_short | Computer Modeling of the Heart for ECG Interpretation—A Review |
| title_sort | computer modeling of the heart for ecg interpretation a review |
| topic | in silico electrophysiology electrocardiogram ECG cardiac disease arrhythmia |
| url | https://www.mdpi.com/2673-3846/2/3/28 |
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