Electrocardiographic imaging of myocardial infarction using heart vector analysis

Ghasemi, Mohsen; Jalali, Ali; Saberi, Hamid; Atarod, Mohammad; Golbayani, Hami; Ghorbanian, Parham; Ghaffari, A.

doi:10.1109/cic.2007.4745563

Cited by 3 publications

(1 citation statement)

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“…BSPMs of patients were provided for the participants. Ghasemi et al were very successful in finding the location and extent of the infarction using only the heart vector and a very simplified model of the distribution of depolarization during systole [90]. Farina et al employed a model-based approach to solve the task [23] and also reached very good results; however, based on the full BSPM.…”

Section: Ischemia and Infarctionmentioning

confidence: 99%

Computer Modeling of the Heart for ECG Interpretation—A Review

Dössel

Luongo

Nagel

et al. 2021

Hearts

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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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Section: Ischemia and Infarctionmentioning

confidence: 99%