Abstract

The study of activation maps using multi-electrode arrays (MEA) can help to understand atrial fibrillation (AF) mechanisms. Activation mapping based on recorded unipolar electrograms (u-EGM) rely on the local activation time (LAT) detector, which has a limited robustness, accuracy, and generally requires manual post-edition. In general, LAT detection ignores spatiotemporal information about activation and conduction conveyed by the relation between signals of the MEA sensor. This work proposes an approach to construct activation maps by simultaneous analysis of u-EGMs from small clusters of MEA electrodes. The algorithm iteratively fits an activation pattern model to the acquired data. Accuracy was evaluated by comparing with audited maps created by expert electrophysiologists from a patient undergoing open-chest surgery during AF. The estimation error was −0.29 ± 6.01 ms (236 maps, 28369 LATs) with high correlation (ρ = 0.93). Therefore, activation maps can be decomposed into local activation patterns derived from fitting an activation model, resulting in smooth and comprehensive high-density activation maps.

Highlights

  • Atrial fibrillation (AF) is one of the most common arrhythmias, especially in elderly people [1]

  • The propagation of cardiac activation is shown by displaying the recorded signals in a matrix related with the location of the electrodes on the multi-electrode array (MEA) sensor [8]

  • Detection of local activation times (LAT) for activation mapping is typically associated to the unipolar electrogram steepest negative slope [9]

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Summary

Introduction

Atrial fibrillation (AF) is one of the most common arrhythmias, especially in elderly people [1]. For high-density simultaneous mapping, a multi-electrode array (MEA) mapping sensor is needed. Reconstruction of activation maps is usually done by combining detected LATs from each of the electrodes on the MEA sensor. This procedure ignores the spatiotemporal information embedded in the u-EGM signal. A less detailed approach (and less time-consuming) represents the cardiac activation propagation as an equivalent source model which uses current sources and densities to calculate the potentials, describing the activation propagation as a uniform double layer (UDL) model [11]. The proposed method iteratively fits the UDL activation pattern model to acquired cardiac activity on small clusters of the MEA sensor and reconstructs the complete activation map by combining the solutions for each cluster

Materials
Methods
Conduction velocity measurement and initial location
Iterative algorithm and map reconstruction
Results
Discussion and conclusion
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