Unipolar electrogram-based voltage mapping with far-field cancellation to improve detection of abnormal atrial substrate during atrial fibrillation.

INTRODUCTION: An important substrate for atrial fibrillation (AF) is fibrotic atrial myopathy. Identifying low voltage, myopathic regions during AF using traditional bipolar voltage mapping is limited by the directional dependency of wave propagation. Our objective was to evaluate directionally independent unipolar voltage mapping, but with far-field cancellation, to identify low-voltage regions during AF. METHODS: In 12 patients undergoing pulmonary vein isolation for AF, high-resolution voltage mapping was performed in the left atrium during sinus rhythm and AF using a roving 20-pole circular catheter. Bipolar electrograms (EGMs) (Bi) < 0.5 mV in sinus rhythm identified low-voltage regions. During AF, bipolar voltage and unipolar voltage maps were created, the latter with (uni-res) and without (uni-orig) far-field cancellation using a novel, validated least-squares algorithm. RESULTS: Uni-res voltage was ~25% lower than uni-orig for both low voltage and normal atrial regions. Far-field EGM had a dominant frequency (DF) of 4.5-6.0 Hz, and its removal resulted in a lower DF for uni-orig compared with uni-res (5.1 ± 1.5 vs. 4.8 ± 1.5 Hz; p < .001). Compared with Bi, uni-res had a significantly greater area under the receiver operator curve (0.80 vs. 0.77; p < .05), specificity (86% vs. 76%; p < .001), and positive predictive value (43% vs. 30%; p < .001) for detecting low-voltage during AF. Similar improvements in specificity and positive predictive value were evident for uni-res versus uni-orig. CONCLUSION: Far-field EGM can be reliably removed from uni-orig using our novel, least-squares algorithm. Compared with Bi and uni-orig, uni-res is more accurate in detecting low-voltage regions during AF. This approach may improve substrate mapping and ablation during AF, and merits further study.

Spatial Relationships of Complex Fractionated Atrial Electrograms and Continuous Electrical Activity to Focal Electrical Sources: Implications for Substrate Ablation in Human Atrial Fibrillation.

OBJECTIVES: This study sought to evaluate the spatial relationships of focal electrical sources (FSs) to complex fractionated atrial electrograms (CFAE) and continuous electrical activity (CEA). BACKGROUND: Fractionated atrial electrograms have been associated with atrial fibrillation (AF) drivers in computational studies and represent ablation targets in the management of persistent AF. METHODS: We included a subset of 66 patients (age: 63 [56, 67] years, 69% persistent AF) with electroanatomic data from the SELECT AF (Selective complex fractionated atrial electrograms targeting for atrial fibrillation) randomized control trial that compared the efficacy of CFAE with CEA ablation in AF patients undergoing pulmonary vein antral ablation. Focal sources were identified based on bipolar electrogram periodicity and QS unipolar electrogram morphology. RESULTS: A total of 77 FSs (median: 1 [1st quartile, 3rd quartile: 1, 2] per patient) were identified most commonly in the pulmonary vein antrum and left atrial appendage. The proportions of FSs inside CFAE and CEA regions were similar (13% vs. 1.3%, respectively; p = 0.13). Focal sources were more likely to be on the border zone of CFAEs than in CEAs (49% vs. 7.8%, respectively; p = 0.012). Following ablation, 53% of patients had ≥1 unablated extrapulmonary vein FS. The median number of unablated FS was higher in patients with AF recurrence post ablation than in patients without (median: 1 [0, 1] vs. 0 [0, 1], respectively; p = 0.026). CONCLUSIONS: One-half of the FSs detected during AF localized to the border of CFAE areas, whereas most of the FSs were found outside CEA areas. CFAE or CEA ablation leaves a number of FS unablated, which is associated with AF recurrence. These findings suggest that many CFAEs may arise from passive wave propagation, remote from FS, which may limit their therapeutic efficacy in AF substrate modification.

An algorithm for rotor tracking in atrial fibrillation using graph search-based periodic peak detection.

Rotors are rotating electrical waves that may sustain atrial fibrillation (AF); thereby providing therapeutic targets for catheter ablation. We propose a method for identifying rotors from circular catheter recordings of bipolar intracardiac electrograms (EGM) during AF. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activations or peaks of interest in each bipolar EGM recorded by a multipolar circular catheter. We then track the activations across catheter bipoles to determine whether they conform to the rotational pattern of a rotor. The performance of the proposed method is tested on simulated bipolar EGM arrays containing rotor activation corrupted by noise and complex aperiodic signal features. The method is shown to perform with high accuracy (up to 98% sensitivity and 100% specificity) in detecting simulated rotors and may serve to guide rotor ablation in patients with AF.

Identification and annotation of multiple periodic pulse trains using dominant frequency and graph search: Applications in atrial fibrillation rotor detection.

Biological signals, such as intracardiac electrograms during atrial fibrillation (AF), can contain multiple periodic components or peaks. We propose a method for identifying individual periodic peak trains in signals containing multiple such periodic sequences. We use dominant frequency-based periodicity detection along with a graph search algorithm to identify the most dominant periodic activation set or peaks of interest. We then remove these peaks and iterate until all periodic sequences are identified. The proposed method is tested on simulated AF intra-cardiac electrograms with periodic activation trains of three distinct frequencies corrupted by noise and complex aperiodic signal features. The method is shown to have high accuracy (up to 100% sensitivity and 100% specificity) in detecting the three individual periodic peak trains. The method has application in biomedical signal analysis, such as detecting the periodic activations of a rotor, amidst other periodic activations during AF.