# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 5 Conclusions  
This study proposed a data fusion framework based on the JDL model and IQ assessment for the processing of EGMs. The framework is designed to locate rotor tips from simulated EGMs in a 2D model of human atrial tissue under atrial fibrillation conditions, while systematically assessing IQ throughout the information processing chain. The framework integrates six processing levels, enabling the merging of EGMs and feature maps derived from multiple characteristics to enhance the accuracy and reliability of rotor tip detection. Additionally, we incorporated situation assessment and risk assessment models using FIS to incorporate expert knowledge and improve decision-making. To evaluate the robustness of the framework, extensive tests were conducted under various noise conditions, simulating real-world challenges in EGM acquisition and processing. A comprehensive set of 13 IQ criteria with corresponding metrics was proposed, with specific criteria assigned to each level of the data fusion process. This approach ensures that IQ is systematically evaluated and enhanced at every stage, providing users with more reliable and actionable information. By integrating IQ assessment and enhancement processes, the framework enables specialists to make better-informed decisions during both EGM acquisition and treatment application.  
The results demonstrated that the proposed framework outperforms traditional rotor tip detection methods based on feature maps or machine learning models, achieving higher accuracy and robustness. The inclusion of optimization and fusion processes, along with IQ assessment, significantly improves the quality of the output. Furthermore, the framework provides users with richer data for decision-making, including IQ evaluations, situation assessments, and risk analyses.  
For future work, we plan to extend the framework to include real-world EGMs with varying levels of quality, further validating its applicability in clinical settings. This will help bridge the gap between simulation-based studies and practical implementation, ultimately enhancing the framework's utility in diagnosing and treating atrial fibrillation.  
Conceptualization, M.A.B., D.H.P.-O., C.M., J.P.U. and C.T.; methodology, M.A.B., J.V., C.M. and J.P.U.; software, M.A.B.; data curation, J.V.; validation, M.A.B. and D.H.P.-O.; formal analysis, M.A.B.; investigation, M.A.B., D.H.P.-O., C.M., J.P.U. and C.T.; writing--original draft preparation, M.A.B., D.H.P.-O. and C.T.; writing--review and editing, M.A.B., D.H.P.-O., J.P.U.  
and C.T.; supervision, D.H.P.-O., J.P.U. and C.T. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Pascual Bravo University Institution through project No. PCT00015.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank SDAS Research group.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AF & Atrial fibrillation \\ EGM & Electrogram \\ IQ & Information quality \\ DF & Dominant frequency \\ JDL & Joint Directors of Laboratories \\ CFAEs & Complex fractionated atrial electrograms \\ PSD & Power spectral density \\
2D & Two-dimensional \\ PLI & Power line interference \\ ApEn & Approximate entropy \\ SampEn & Sample entropy \\ ShEn & Shannon entropy \\ HCI & Human-computer interaction \\ STD & Standard deviation \\ PSO & Particle swarm optimization \\ FIS & Fuzzy interference system \\ SVR & Support vector regression \\ \end{tabular}