Vraka Aikaterini, Hornero Fernando, Bertomeu-González Vicente, Osca Joaquín, Alcaraz Raúl, Rieta José J
BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, 46022 Valencia, Spain.
Cardiac Surgery Department, Hospital Universitari i Politecnic La Fe, 46026 Valencia, Spain.
Entropy (Basel). 2020 Feb 19;22(2):232. doi: 10.3390/e22020232.
Atrial fibrillation (AF) is currently the most common cardiac arrhythmia, with catheter ablation (CA) of the pulmonary veins (PV) being its first line therapy. Ablation of complex fractionated atrial electrograms (CFAEs) outside the PVs has demonstrated improved long-term results, but their identification requires a reliable electrogram (EGM) fractionation estimator. This study proposes a technique aimed to assist CA procedures under real-time settings. The method has been tested on three groups of recordings: Group 1 consisted of 24 highly representative EGMs, eight of each belonging to a different AF Type. Group 2 contained the entire dataset of 119 EGMs, whereas Group 3 contained 20 pseudo-real EGMs of the special Type IV AF. Coarse-grained correlation dimension (CGCD) was computed at epochs of 1 s duration, obtaining a classification accuracy of 100% in Group 1 and 84.0-85.7% in Group 2, using 10-fold cross-validation. The receiver operating characteristics (ROC) analysis for highly fractionated EGMs, showed 100% specificity and sensitivity in Group 1 and 87.5% specificity and 93.6% sensitivity in Group 2. In addition, 100% of the pseudo-real EGMs were correctly identified as Type IV AF. This method can consistently express the fractionation level of AF EGMs and provides better performance than previous works. Its ability to compute fractionation in short-time can agilely detect sudden changes of AF Types and could be used for mapping the atrial substrate, thus assisting CA procedures under real-time settings for atrial substrate modification.
心房颤动（AF）是目前最常见的心律失常，肺静脉（PV）导管消融（CA）是其一线治疗方法。对PV以外的复杂碎裂心房电图（CFAE）进行消融已显示出更好的长期效果，但其识别需要可靠的电图（EGM）碎裂估计器。本研究提出了一种旨在辅助实时环境下CA手术的技术。该方法已在三组记录上进行了测试：第1组由24个具有高度代表性的EGM组成，每种类型的AF各有8个。第2组包含119个EGM的完整数据集，而第3组包含20个特殊IV型AF的伪真实EGM。在持续1秒的时间段内计算粗粒度相关维数（CGCD），使用10折交叉验证，第1组的分类准确率为100%，第2组为84.0 - 85.7%。对高度碎裂的EGM进行的受试者工作特征（ROC）分析显示，第1组的特异性和敏感性均为100%，第2组的特异性为87.5%，敏感性为93.6%。此外，100%的伪真实EGM被正确识别为IV型AF。该方法能够一致地表达AF EGM的碎裂水平，并且比以前的工作具有更好的性能。它在短时间内计算碎裂的能力可以灵活地检测AF类型的突然变化，并可用于绘制心房基质图，从而在实时环境下辅助CA手术进行心房基质修改。
