Highest Dominant Frequency and Rotor Sites are Robust Markers for Atrial Driver Location in Non:invasive Mapping of Atrial Fibrillation

Rodrigo, Miguel; Climent, Andreu Μ.; Liberos, Alejandro; Fernández‐Avilés, Francisco; Berenfeld, Omer; Atienza, Felipe; Guillem, María S.

doi:10.22489/cinc.2016.004-418

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…Many methods have been introduced in the literature to analyze EGMs. These methods are generally in three categories: signal processing techniques such as dominant frequency analysis and recurrence quantification analysis [ 8 , 9 , 10 , 11 , 12 , 13 , 21 , 22 ], machine learning and pattern recognition techniques such as principle component analysis, independent component analysis, linear discriminant analysis and quadratic discriminant analysis [ 15 , 16 , 19 , 20 ], and geometric approaches that infer the travel paths of electrical waves using LATs [ 17 , 23 , 24 , 25 , 26 , 27 ]. The proposed method belongs to the geometric category, as it tracks the most probable travel path using LATs, and identifies the focal source based on geodesic distances.…”

Section: Discussionmentioning

confidence: 99%

“…Cantwell et al [ 7 ] surveyed the algorithms designed for identifying local activation times (LATs) and calculating the conduction velocity (CV) of electrical waves, and pointed out that estimating the uncertainty associated with the CV computation remains a challenge. In addition, the dominant frequency (DF) mapping method was used in a few studies to localize abnormal pacing sites of high frequency during AF [ 8 , 9 , 10 ]. However, Salinet et al showed that targeting the ablation site using DF could be unreliable [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

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An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Koneshloo

2020

Bioengineering

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Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Koneshloo

2020

Bioengineering

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“…Set G ,q|P = NaN and C ,q|P = NaN for 1 ≤ , q ≤ Q with = i 1 . 3 2. Sort the elements in C i1 according to their G-causality strength:…”

Section: First Iteration: Processing the Sons Of The Root Nodementioning

confidence: 99%

“…Several authors have investigated the inference of causality relationships among physiological signals. In particular, in electrocardiography the use of partial directed coherence to investigate propagation patterns in intra-cardiac signals was considered in [1], whereas Granger causality maps were built in [2,3]. In this paper, we propose a novel hierarchical causality approach to guide cardiologists towards candidate areas for cathether ablation and discriminate between normal (sinus rhythm) and abnormal propagation (rotors).…”

Section: Introductionmentioning

confidence: 99%

Causality analysis of atrial fibrillation electrograms

Luengo

Munoz

Elvira

2015

2015 Computing in Cardiology Conference (CinC)

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Multi-channel intracardiac electrocardiograms (electrograms) are sequentially acquired during heart surgery performed on patients with sustained atrial fibrillation (AF) to guide radio frequency catheter ablation. These electrograms are used by cardiologists to determine candidate areas for ablation (e.g., areas corresponding to high dominant frequencies or complex electrograms). In this paper, we introduce a novel hierarchical causality analysis method for the multi-output sequentially acquired electrograms. The causal model obtained provides important information regarding delays among signals as well as the direction and strength of their causal connections. The tool developed may ultimately serve to guide cardiologists towards candidate areas for catheter ablation. Preliminary results on synthetic signals are used to validate the proposed approach.

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Highest Dominant Frequency and Rotor Sites are Robust Markers for Atrial Driver Location in Non:invasive Mapping of Atrial Fibrillation

Abstract: Inverse-computed Dominant Frequency (DF) and rotor maps have been proposed as non-invasive mapping techniques to locate atrial drivers maintaining atrial fibrillation (AF

Cited by 2 publications

References 7 publications

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

Causality analysis of atrial fibrillation electrograms

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