Abstract

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.

Highlights

  • This article reviews research aimed at building a bridge between computerized modeling of the electrophysiology of the human heart and the analysis of the electrocardiogram (ECG)

  • Even though the focus of an article of Bukhari et al was on the changes in the T-wave during dialysis, this article reported on the heterogeneity that is needed to obtain a realistic T-wave in healthy hearts

  • All the effects described in the literature that have an influence on the morphology of the Pwave of atrial fibrillation (AFib) patients are outlined

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Summary

Introduction

This article reviews research aimed at building a bridge between computerized modeling of the electrophysiology of the human heart and the analysis of the electrocardiogram (ECG). Calculation times for computing the spread of depolarization and repolarization, the lead field matrix and the body surface potentials including the ECG strongly depend on the methods employed: highly detailed cell models versus simplified phenomenological models, high versus low spatial resolutions, etc. They can range from one day down to one second. Fast calculation times are important for the researcher aiming at the identification of new features in the ECG, for creating a training dataset for machine learning and for personalization of a heart model They are not relevant any more if, for example, a machine learning algorithm is used in clinics

The QRS Complex and the Purkinje Tree
The T-Wave and the Repolarization
The P-Wave
Modeling Rhythmic Features and Heart Rate Variability
Modeling Inter- and Intra-Patient Variability
Ischemia and Infarction
Ventricular Ectopic Beats and Extrasystoles
Ventricular Tachycardia
Cardiomyopathy
Bundle Branch Blocks
Atrial Ectopic Beats
Genetic Diseases and Channelopathies
Imbalance of Electrolytes
3.10. Drug-Induced Changes in the ECG
Options of Modeling for Better Interpretation of the ECG
Summary and Outlook
Findings
Methods
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