# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 1 Introduction
To the best of our knowledge, no studies have comprehensively analyzed the combined effects of multiple noise sources and the robustness of feature extraction techniques used for ablation zone detection while incorporating information quality (IQ) assessments at different stages of EGM processing. In this context, data fusion techniques provide a powerful framework to enhance signal interpretation and robustness. The Joint Directors of Laboratories (JDL) model, the most widely adopted framework for data fusion, provides a structured methodology integrating statistical and computational techniques across multiple processing levels to enhance situational understanding. Initially proposed with four levels in [27], the model was later expanded to six levels in [28]. As illustrated in Figure 1, the JDL model operates through a data bus connecting these six processing levels. The JDL model organizes data fusion into six hierarchical levels, where the lower levels (0 and 1) process numerical data to determine object position, kinematics, and identity, while the higher levels (2 and 3) interpret context for decision-making, using symbolic and probabilistic techniques such as logic, evidence theory, and neural networks [29]. Specifically, level 0 (preprocessing) prepares raw data through noise reduction, calibration, and alignment using methods like Kalman filtering and Fourier transforms. Level 1 (object refinement) identifies and tracks objects, updating their states through classification, tracking, and data association. Level 2 (situation refinement) analyzes interactions and behaviors by integrating spatial and temporal information, employing Bayesian networks and Markov logic networks. Level 3 (impact/risk refinement) assesses potential outcomes and risks using predictive models such as hidden Markov models and dynamic Bayesian networks. Level 4 (process refinement) optimizes resource allocation and system adjustments through adaptive filtering and optimization techniques, ensuring continuous evaluation of the data fusion process. Finally, level 5 (user refinement) supports decision-making by effectively presenting fused data using decision theory and user modeling. This structured approach enhances situational awareness and facilitates informed decision-making across various applications. This model has been applied in bioinformatics, infrastructure, and other domains [30; 31; 32; 33]. This work proposes a data fusion framework based on the JDL functional model [34] and IQ assessment for rotor localization in persistent AF. Our approach utilizes simulated ECMs in a two-dimensional (2D) atrial tissue model. Our main contributions are the following: (i) Integrating a multi-level data fusion approach to enhance rotor tip estimation. (ii) The incorporation of an IQ-driven optimization process to improve signal processing robustness against noise and artifacts. (iii) The validation of the proposed framework through extensive simulations under different perturbation scenarios. This novel approach aims to support clinical decision-making by providing more accurate and reliable rotor localization for ablation therapy.