Comparing Simulated Electrocardiograms of Different Stages of Acute Cardiac Ischemia

Wilhelms, Mathias; Dössel, Olaf; Seemann, Gunnar

doi:10.1007/978-3-642-21028-0_2

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…On the one hand, this is not surprising due to their larger heart surface and thus signal. On the other hand, this finding seems to contradict the results by Wilhelms et al [ 7 , 26 , 35 ] at first sight. In their studies, subendocardial ischemia caused ST depression, which changed into pronounced ST elevation for larger transmural ischemic regions.…”

Section: Discussioncontrasting

confidence: 81%

“…The course of these parameters as a function of the ZF and the position within the ventricular wall was determined by conducting acCELLerate simulations as described in [ 13 ]. To this end, Weiss's extensions [ 7 , 14 , 26 ] to the ten Tusscher cell model that account for the three main biochemical effects of ischemia (extracellular hyperkalemia, acidosis, and hypoxia) were utilized. The model represents ischemic conditions at 10 minutes after onset (phase Ia, stage 2).…”

Section: Methodsmentioning

confidence: 99%

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ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation

Loewe

Schulze

Jiang

et al. 2015

BioMed Research International

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In case of chest pain, immediate diagnosis of myocardial ischemia is required to respond with an appropriate treatment. The diagnostic capability of the electrocardiogram (ECG), however, is strongly limited for ischemic events that do not lead to ST elevation. This computational study investigates the potential of different electrode setups in detecting early ischemia at 10 minutes after onset: standard 3-channel and 12-lead ECG as well as body surface potential maps (BSPMs). Further, it was assessed if an additional ECG electrode with optimized position or the right-sided Wilson leads can improve sensitivity of the standard 12-lead ECG. To this end, a simulation study was performed for 765 different locations and sizes of ischemia in the left ventricle. Improvements by adding a single, subject specifically optimized electrode were similar to those of the BSPM: 2–11% increased detection rate depending on the desired specificity. Adding right-sided Wilson leads had negligible effect. Absence of ST deviation could not be related to specific locations of the ischemic region or its transmurality. As alternative to the ST time integral as a feature of ST deviation, the K point deviation was introduced: the baseline deviation at the minimum of the ST-segment envelope signal, which increased 12-lead detection rate by 7% for a reasonable threshold.

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Section: Discussioncontrasting

confidence: 81%

Section: Methodsmentioning

confidence: 99%

ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation

Loewe

Schulze

Jiang

et al. 2015

BioMed Research International

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“…In our future work, infarct border zone can be included in our simulation setting. We will also consider extending the presented work to imaging ischemic regions using previously reported cellular models of ischemia [80], [81]. …”

Section: Discussionmentioning

confidence: 99%

Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach

Rahimi

Sapp

et al. 2016

IEEE Trans. Med. Imaging

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Noninvasive cardiac electrophysiological (EP) imaging aims to mathematically reconstruct the spatiotemporal dynamics of cardiac sources from body-surface electrocardiographic (ECG) data. This ill-posed problem is often regularized by a fixed constraining model. However, a fixed-model approach enforces the source distribution to follow a pre-assumed structure that does not always match the varying spatiotemporal distribution of actual sources. To understand the model-data relation and examine the impact of prior models, we present a multiple-model approach for volumetric cardiac EP imaging where multiple prior models are included and automatically picked by the available ECG data. Multiple models are incorporated as an Lp-norm prior for sources, where p is an unknown hyperparameter with a prior uniform distribution. To examine how different combinations of models may be favored by different measurement data, the posterior distribution of cardiac sources and hyperparameter p is calculated using a Markov Chain Monte Carlo (MCMC) technique. The importance of multiple-model prior was assessed in two sets of synthetic and real-data experiments, compared to fixed-model priors (using Laplace and Gaussian priors). The results showed that the posterior combination of models (the posterior distribution of p) as determined by the ECG data differed substantially when reconstructing sources with different sizes and structures. While the use of fixed models is best suited in situations where the prior assumption fits the actual source structures, the use of an automatically adaptive set of models may have the ability to better address model-data mismatch and to provide consistent performance in reconstructing sources with different properties.

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“…Modeling Challenge References healthy heart-QRS modeling the Purkinje tree [1][2][3][4][5][6][7][8][9] healthy heart-T-wave modeling heterogeneity of repolarization [10][11][12][13] healthy heart-P-wave modeling sinus node excitation and pathways from right to left atrium, anatomical variability [14][15][16][17][18][19][20][21][22] ischemia and infarction modeling the effect of hyperkalemia, acidosis, hypoxia and cell-to-cell uncoupling [23][24][25][26][27][28][29][30] ventricular ectopic beats localization with 12-lead ECG [31][32][33][34] ventricular tachycardia localization of exit points with 12-lead ECG [29,35] cardiomyopathy modeling typical changes of QRS-and T-wave [36] bundle branch blocks LBBB and RBBB modeling asynchrony [37][38][39][40] atrial ectopic beats localization with 12-lead ECG [32][33][34] atrial tachycardia, flutter modeling all types of flutter…”

Section: Topicmentioning

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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Comparing Simulated Electrocardiograms of Different Stages of Acute Cardiac Ischemia

Cited by 8 publications

References 13 publications

ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation

ECG-Based Detection of Early Myocardial Ischemia in a Computational Model: Impact of Additional Electrodes, Optimal Placement, and a New Feature for ST Deviation

Examining the Impact of Prior Models in Transmural Electrophysiological Imaging: A Hierarchical Multiple-Model Bayesian Approach

Computer Modeling of the Heart for ECG Interpretation—A Review

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