Novel algorithm for accelerated electroanatomic mapping and prediction of earliest activation of focal cardiac arrhythmias using mathematical optimization

Abstract

BackgroundPremature beats (PBs) are a common finding in patients suffering from structural heart disease, but they can also be present in healthy individuals. Catheter ablation represents a suitable therapeutic approach. However, the exact localization of the origin can be challenging, especially in cases of low PB burden during the procedure.ObjectiveThe aim of this study was to develop an automated mapping algorithm on the basis of the hypothesis that mathematical optimization would significantly accelerate the localization of earliest activation.MethodsThe algorithm is based on iterative regression analyses. When acquiring local activation times (LATs) within a 3-dimensional anatomic map of the corresponding heart chamber, this algorithm is able to identify that exact position where a next LAT measurement adds maximum information about the predicted site of origin. Furthermore, on the basis of the acquired LAT measurements, the algorithm is able to predict earliest activation with high accuracy.ResultsA systematic retrospective analysis of the mapping performance comparing the operator with simulated search processes by the algorithm within 17 electroanatomic maps of focal spreading arrhythmias revealed a highly significant reduction of necessary LAT measurements from 55 ± 8.8 to 10 ± 0.51 (n = 17; P < .0001).ConclusionOn the basis of mathematical optimization, we developed an algorithm that is able to reduce the number of LAT measurements necessary to locate the site of earliest activation. This algorithm might significantly accelerate the mapping procedure by guiding the operator to the optimal position for the next LAT measurement. Furthermore, the algorithm would be able to predict the site of origin with high accuracy early during the mapping procedure.

An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis

“…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.…”

“…Apart from focusing exclusively on the information provided by the electrograms, there are different perspectives applied to extract information from EGMs. In the study by Weber et al, iterative optimization is done to predict the earliest activation using LATs within a 3-dimensional anatomic map of heart chamber [ 23 ]. Many studies have been done to locate the AF focal source [ 24 , 25 , 26 ], where the geodesic information is combined with EGMs to infer the electrical activities in arrythmia.…”

A Divergence-Based Approach for the Identification of Atrial Fibrillation Focal Drivers From Multipolar Mapping: A Computational Study

“…These features may thus be used to inform the mapping strategy in order to accelerate the detection of these critical sites in the absence of human pattern recognition. Few previous studies have proposed algorithms to navigate the mapping catheter toward focal or early activation sites (Roney et al, 2014;Weber et al, 2017;Ganesan et al, 2019). An algorithm based on iterative regression analyses displayed high accuracy to predict the earliest activation site during focal tachycardias, requiring a significantly lower number of mapping points with respect to an operator-guided mapping (Weber et al, 2017).…”

Computer Assisted Localization of a Heart Arrhythmia

“…Related work. We found one prior approach to localizing arrhythmia using optimization [5]. The approach is similar in principle, and uses optimization to recommend the next point to sample by the operator.…”

“…The approach is similar in principle, and uses optimization to recommend the next point to sample by the operator. The key differentiating factor is that [5] assumes the existence of an anatomical map; the approach is predicated on being able to solve a linear regression problem that uses every available nodal point as a potential origin, and then pick the one that best fits the available data. In contrast, we make no assumptions about the existence of anatomical maps; our proposals are based only on observations taken by the operator, and we can help patients who have undergone no prior mapping studies.…”