Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy

Pereira, H; Niederer, Steven; Rinaldi, Christopher A.

doi:10.1093/europace/euaa165

Cited by 30 publications

(19 citation statements)

References 79 publications

(140 reference statements)

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“…It is the easiest tool to detect left bundle branch block (LBBB) and select patients for CRT. The expansion up to 300 electrodes allows a 3D visualization of onset of activation and propagation captured from the body surface [ 22 ]. A correlation of low voltage and scar has been shown [ 23 , 24 ].…”

Section: Finding the Optimal LV Lead Positionmentioning

confidence: 99%

Optimal CRT Implantation—Where and How To Place the Left-Ventricular Lead?

Butter

Georgi

Stockburger³

2021

Curr Heart Fail Rep

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Purpose of Review Cardiac resynchronization therapy (CRT) represents a well-established and effective non-pharmaceutical heart failure (HF) treatment in selected patients. Still, a significant number of patients remain CRT non-responders. An optimal placement of the left ventricular (LV) lead appears crucial for the intended hemodynamic and hence clinical improvement. A well-localized target area and tools that help to achieve successful lead implantation seem to be of utmost importance to reach an optimal CRT effect. Recent Findings Recent studies suggest previous multimodal imaging (CT/cMRI/ECG torso) to guide intraprocedural LV lead placement. Relevant benefit compared to empirical lead optimization is still a matter of debate. Technical improvements in leads and algorithms (e.g., multipoint pacing (MPP), adaptive algorithms) promise higher procedural success. Recently emerging alternatives for ventricular synchronization such as conduction system pacing (CSP), LV endocardial pacing, or leadless pacing challenge classical biventricular pacing. Summary This article reviews current strategies for a successful planning, implementation, and validation of the optimal CRT implantation. Pre-implant imaging modalities offer promising assistance for complex cases; empirical lead positioning and intraoperative testing remain the cornerstone in most cases and ensure a successful CRT effect.

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Section: Finding the Optimal LV Lead Positionmentioning

confidence: 99%

Optimal CRT Implantation—Where and How To Place the Left-Ventricular Lead?

Butter

Georgi

Stockburger³

2021

Curr Heart Fail Rep

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“…However, validating ECGI continues to be a challenge [ 82 ]. Several limitations of ECGI are complicating the validation and the incorporation into the clinical practice [ 83 ]. For example, implementation choices and the type of application for which ECGI is used can largely influence ECGI and complicate the validation [ 82 ].…”

Section: The Use Of Multiple Imaging Modalitiesmentioning

confidence: 99%

“…For example, implementation choices and the type of application for which ECGI is used can largely influence ECGI and complicate the validation [ 82 ]. Moreover, performing ECGI can be difficult and requires adequate training of personnel, for both the measurements and interpretations of ECGI [ 83 ]. Keeping these shortcomings in mind, we will address the possible applications for ECGI, and discuss the first results of ECGI in IVF patients.…”

Section: The Use Of Multiple Imaging Modalitiesmentioning

confidence: 99%

State-of-the-Art Multimodality Imaging in Sudden Cardiac Arrest with Focus on Idiopathic Ventricular Fibrillation: A Review

Verheul

Groeneveld

Kirkels

et al. 2022

JCM

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Idiopathic ventricular fibrillation is a rare cause of sudden cardiac arrest and a diagnosis by exclusion. Unraveling the mechanism of ventricular fibrillation is important for targeted management, and potentially for initiating family screening. Sudden cardiac arrest survivors undergo extensive clinical testing, with a growing role for multimodality imaging, before diagnosing “‘idiopathic” ventricular fibrillation. Multimodality imaging, considered as using multiple imaging modalities as diagnostics, is important for revealing structural myocardial abnormalities in patients with cardiac arrest. This review focuses on combining imaging modalities (echocardiography, cardiac magnetic resonance and computed tomography) and the electrocardiographic characterization of sudden cardiac arrest survivors and discusses the surplus value of multimodality imaging in the diagnostic routing of these patients. We focus on novel insights obtained through electrostructural and/or electromechanical imaging in apparently idiopathic ventricular fibrillation patients, with special attention to non-invasive electrocardiographic imaging.

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“…Cardiovascular diseases are one of the main causes of mortality across the world, which impose a heavy burden on society [ 1 , 2 , 3 ]. Cardiac arrhythmia, which is known as an abnormal heart rhythm, is considered as a very important category of cardiovascular disorders [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

Andayeshgar

Abdali-Mohammadi

Sepahvand

et al. 2022

IJERPH

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Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.

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Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy

Cited by 30 publications

References 79 publications

Optimal CRT Implantation—Where and How To Place the Left-Ventricular Lead?

Optimal CRT Implantation—Where and How To Place the Left-Ventricular Lead?

State-of-the-Art Multimodality Imaging in Sudden Cardiac Arrest with Focus on Idiopathic Ventricular Fibrillation: A Review

Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals

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