Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone

Ruipérez-Campillo, Samuel; Castrejón, Sergio; Martínez, Marcel; Cervigón, Raquel; Meste, Olivier; Merino, José Luís; Millet, José; Castells, Francisco

doi:10.1016/j.cmpb.2021.105932

Cited by 6 publications

(3 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study we consider slow velocity conduction regions as the main discriminant factor to classify different types of macroreentrant atrial tachyarrhythmias [7]. Thus, a variant of a synthetic model based on this principle [7] [8] is used to create 8,000 AFL VCG loops, of 8 different groups, which correspond to 4 different regions of slow conduction, and two propagation directionsclockwise (CW) and counterclockwise (CCW).…”

Section: Methodsmentioning

confidence: 99%

Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Ruiperez-Campillo¹,

Millet²,

Castells"³

2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

Accurate non-invasive diagnoses in the context of cardiac diseases are problems that hitherto remain unresolved. We propose an unsupervised classification of atrial flutter (AFL) using dimensional transforms of ECG signals in high dimensional vector spaces. A mathematical model is used to generate synthetic signals based on clinical AFL signals, and hierarchical clustering analysis and novel machine learning (ML) methods are designed for the unsupervised classification. Metrics and accuracy parameters are created to assess the performance of the model, proving the power of this novel approach for the diagnosis of AFL from ECG using innovative AI algorithms.

show abstract

Section: Methodsmentioning

confidence: 99%

Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Ruiperez-Campillo¹,

Millet²,

Castells"³

2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

show abstract

“…On the other hand, Medi et al were able to separate a few classes of atypical AFlut just from the ECG without any modeling [94] such as, for example, focal versus macro-re-entry and right atrial versus left atrial macro-re-entry. An approach with similar objectivesclassification of macro-re-entrant atrial tachycardia-but without any modeling should be mentioned: Ruiperez-Campillo et al analyzed loops of the atrial vectorcardiogram, created "archetypes" of the loops of the four most frequent types of AFlut and determined the similarity of the vectorcardiogram loop of a patient with the archetypes [95].…”

Section: Atrial Tachycardia Flutter and Fibrillationmentioning

confidence: 99%

Computer Modeling of the Heart for ECG Interpretation—A Review

Dössel

Luongo

Nagel

et al. 2021

Hearts

View full text Add to dashboard Cite

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.

show abstract

“…To identify and characterise the area that needs to be ablated, a multielectrode and an electroanatomic navigator are used to perform a 3D mapping of the heart cham-ber. Furthermore, the location of the device is essential as perpetuation mechanisms of slow conduction regions may reveal the ablation region for different diseases such as AFL [2,3]. Another example is given by complex fractionated EGMs detected during substrate mapping during AF, that can reflect complex etiologies that need to be well defined [4].…”

Section: Introductionmentioning

confidence: 99%

Novel Method for Orientation-Independent Analysis in Equi-Spaced Multi-Electrode Arrays

Segarra¹,

Ruipérez-Campillo²,

Castells³

et al. 2022

Computing in Cardiology Conference (CinC)

View full text Add to dashboard Cite

The diagnosis and treatment of cardiac arrhythmias relies on catheter recordings, that may be inefficient because of the continued use of the bipolar processing and analysis techniques of traditional catheters, missing the potential of the novel matrix catheters. This results in the need of more processing of the signals and longer cardiac scans to obtain accurate information about the state of the tissue being analysed. This study proposes a new clique configuration to compute omnipolar EGM (oEGM) in multielectrode array catheters to obtain parameters of interest in a more robust and accurate manner. Numerous simulations with varying input parameters are designed to emulate the propagation of electrical activity on the cardiac tissue surface captured by the catheter and characterise the differences between the classic method of omnipolar analysis (triangular clique) and our proposed new method (cross clique). The results show that the cross clique is more robust to variations in the direction of wave propagation, and more accurate in the estimation of the local activation time (LAT).

show abstract

Non-invasive characterisation of macroreentrant atrial tachycardia types from a vectorcardiographic approach with the slow conduction region as a cornerstone

Cited by 6 publications

References 39 publications

Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Computer Modeling of the Heart for ECG Interpretation—A Review

Novel Method for Orientation-Independent Analysis in Equi-Spaced Multi-Electrode Arrays

Contact Info

Product

Resources

About