Bai Baodan, Li Xiaoou, Yang Cuiwei, Chen Xinrong, Wang Xuan, Wu Zhong
School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 200433, China.
Engineering Research Center of Universities of Shanghai for Wearable Medical Technology and Instrument, Shanghai 200433, China.
Technol Health Care. 2019;27(S1):287-300. doi: 10.3233/THC-199027.
Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Early diagnosis and intervention of AF is essential to avert the further fatality. The technique of noninvasive electrical mapping, especially the body surface potential mapping (BSPM), has a more practical application in the study of predicting AF, when compared with the invasive electrical mapping methods such as the epicardial mapping and interventional catheter mapping. However, the prediction of AF with noninvasive signals has been inadequately studied. Thus, the aim of this paper was to analyze the properties of atrial dynamic system based on the noninvasive BSPM signals (BSPMs), using the recurrence complex network, and consequently to evaluate its role in predicting the recurrence of AF in clinical aspect.
Twelve patients with persistent AF were included in this study. Their preoperative and postoperative BSPMs were recorded. Initially, the preoperative BSPMs were transformed into the recurrence complex network to characterize the complexity property of the atria. Subsequently, the parameters of recurrence ratio (REC), determinism (DET), entropy of the diagonal structure distribution (ENTR), and laminarity (LAM) were calculated. Furthermore, the difference in the parameters in the four regions of the body and the difference obtained from the dominant frequency (DF) method were compared. Finally, the results obtained for the atrial dynamic system complexity from a 12-lead electrocardiogram (ECG) from the BSPMs were discussed.
Our study revealed that the patients whose REC is greater than an average threshold, and with a lower LAM presented a much higher possibility of AF recurrence, after the AF surgery.
The recurrence complex network is a useful and convenient way to evaluate the nonlinear properties of the BSPMs in patients with AF. It has good immunity to the lead position and has a potential role in the understanding of predicting the recurrence of AF.
心房颤动（AF）是最常见的持续性心律失常类型。AF的早期诊断和干预对于避免进一步的死亡至关重要。与有创电标测方法（如心外膜标测和介入导管标测）相比，无创电标测技术，尤其是体表电位标测（BSPM），在AF预测研究中具有更实际的应用价值。然而，利用无创信号预测AF的研究还不够充分。因此，本文的目的是基于无创BSPM信号（BSPMs），利用递归复杂网络分析心房动态系统的特性，从而在临床方面评估其在预测AF复发中的作用。
本研究纳入了12例持续性AF患者。记录他们术前和术后的BSPMs。首先，将术前BSPMs转换为递归复杂网络以表征心房的复杂性特征。随后，计算递归率（REC）、确定性（DET）、对角结构分布熵（ENTR）和层流性（LAM）等参数。此外，比较身体四个区域参数的差异以及从主频（DF）方法获得的差异。最后，讨论从BSPMs的12导联心电图（ECG）获得的心房动态系统复杂性结果。
我们的研究表明，REC大于平均阈值且LAM较低的患者在AF手术后AF复发的可能性要高得多。
递归复杂网络是评估AF患者BSPMs非线性特性的一种有用且便捷的方法。它对导联位置具有良好的抗干扰能力，在理解预测AF复发方面具有潜在作用。
