Luongo G, Azzolin L, Rivolta M W, Sassi R, Martinez J P, Laguna P, Dossel O, Loewe A
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:410-413. doi: 10.1109/EMBC44109.2020.9176135.
Atrial fibrillation (AF) is an irregular heart rhythm due to disorganized atrial electrical activity, often sustained by rotational drivers called rotors. In the present work, we sought to characterize and discriminate whether simulated single stable rotors are located in the pulmonary veins (PVs) or not, only by using non-invasive signals (i.e., the 12-lead ECG). Several features have been extracted from the signals, such as Hjort descriptors, recurrence quantification analysis (RQA), and principal component analysis. All the extracted features have shown significant discriminatory power, with particular emphasis to the RQA parameters. A decision tree classifier achieved 98.48% accuracy, 83.33% sensitivity, and 100% specificity on simulated data.Clinical Relevance-This study might guide ablation procedures, suggesting doctors to proceed directly in some patients with a pulmonary veins isolation, and avoiding the prior use of an invasive atrial mapping system.
心房颤动（AF）是一种由于心房电活动紊乱导致的心律不齐，通常由称为转子的旋转驱动因素维持。在本研究中，我们试图仅通过使用非侵入性信号（即12导联心电图）来表征和区分模拟的单个稳定转子是否位于肺静脉（PVs）中。已经从信号中提取了几个特征，如 Hjort 描述符、递归定量分析（RQA）和主成分分析。所有提取的特征都显示出显著的区分能力，尤其强调RQA参数。决策树分类器在模拟数据上的准确率达到98.48%，灵敏度达到83.33%，特异性达到100%。临床意义——本研究可能会指导消融手术，建议医生对一些患者直接进行肺静脉隔离，避免事先使用侵入性心房标测系统。
