# Rotor Location During Atrial Fibrillation: A Framework Based on Data Fusion and Information Quality
## 1 Introduction  
Atrial fibrillation (AF) is one of the most common cardiac pathologies worldwide, with an estimated prevalence of 1.5-2% [1]. Catheter ablation has emerged as a widelyused therapeutic alternative to pharmacological management, particularly in cases where medical treatment has proven ineffective [2]. While pulmonary vein isolation has demonstrated efficacy in treating paroxysmal AF, its success rate remains limited in persistent AF cases [3]. This discrepancy has increased interest in identifying the specific localization of the arrhyththmogenic sources that sustain AF [4]. One proposed mechanism involves rotors, which are spiral waves that revolve around a singular point known as the rotor tip. These structures have been hypothesized as key drivers of AF, continuously reactivating atrial tissue and sustaining the arrhythmia [5]. According to the rotor hypothesis, AF is maintained by one or several rotor-type functional reentries, making them key targets for ablation. Therefore, identifying and localizing rotor sites is crucial, as ablation targeting them has been proposed to disrupt AF-sustaining mechanisms and improve treatment outcomes. Clinical evidence supports the pivotal role that rotors play in the persistence of AF [6; 7; 8]. Despite the significance of rotors, their precise locations remain undetermined. Electrophysiological procedures attempt to detect rotor positions by analyzing atrial electrical activity through catheter recordings known as electrograms (EGMs). Consequently, AF treatment strategies based on rotor ablation rely on the accurate identification of rotor tips by EGM processing. Numerous studies have explored the identification of ablation zones by analyzing EGMs, yet this remains an open area of research. Some studies have linked the occurrence of complex fractionated atrial electrograms (CFAEs) with the rotor tip [9; 10; 11], suggesting that these signals could serve as potential ablation landmarks for persistent AF treatment [12; 13; 14]. However, this process requires multiple simultaneous recordings across the atria, exposing the signals to significant noise and artifacts. Various techniques have been proposed for EGM processing to determine ablation zones, including dominant frequency (DF), approximate entropy (ApEn), sample entropy (SampEn), and time-frequency characteristics [10; 15; 16; 17]. While these approaches perform well under controlled conditions with minimal disturbances, their effectiveness diminishes in real-world scenarios due to noise and artifacts in recorded EGMs [4; 18; 19; 20]. Common sources of noise and artifacts in physiological signals include high-frequency interference, power grid noise, motion artifacts, and electrode contact loss. Although signal processing techniques exist to mitigate these disturbances, they may alter relevant information about the underlying arrhyththmogenic mechanisms, potentially affecting the accuracy of rotor localization [21; 22; 23; 24; 25; 26]. In clinical settings, careful consideration must be given to noise sources and artifacts when designing signal processing tools. Despite advancements in filtering and noise reduction, preprocessing steps can still impact the quality of extracted quantitative information. Achieving an optimal balance between noise suppression and information preservation remains a critical challenge for reliable rotor detection based on EGMs. Studies have assessed the influence of noise on EGM characterization using entropy estimators, considering various contamination sources such as signal saturation, poor electrode contact, and far-field activity. The findings indicate that selectively eliminating low-fidelity EGMs significantly enhances the density and stability of rotor tip detection [20]. Computational approaches have been developed to address these challenges and improve real-world applicability. However, denoising and artifact suppression inevitably affect signal quality, potentially altering extracted quantitative features such as frequency content, entropy, and energy.