Key result
Cardiac electrophysiology modeling improves ECG interpretation, non-invasively localizes disease, and generates machine learning databases.
Why the study?
Computer modeling of cardiac electrophysiology has progressed significantly, prompting a review of advances in simulating ECGs from depolarization and repolarization waves and their impact on ECG interpretation.
Computer modeling of cardiac electrophysiology offers significant potential to improve ECG interpretation, localize diseased areas, and generate training data for machine learning algorithms.
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.
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Dössel et al. (2021) conducted a review in ECG interpretation and cardiac electrophysiology. Computer modeling of the heart was evaluated. Computer modeling of cardiac electrophysiology can improve ECG interpretation, support quantitative analysis, non-invasively localize diseased areas, and generate large databases for machine learning.
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