OBJECTIVE: 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.
METHOD: 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.
RESULTS: 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.
CONCLUSIONS: 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.
[1]Shanghai Univ Med & Hlth Sci, Sch Med Instruments, Shanghai 200433, Peoples R China.
[2]Engn Res Ctr Univ Shanghai Wearable Med Technol &, Shanghai 200433, Peoples R China.
[3]Fudan Univ, Dept Elect Engn, Shanghai 200433, Peoples R China.
[4]Fudan Univ, Digital Med Res Ctr, Sch Basic Med Sci, Shanghai 200032, Peoples R China.
[5]Shanghai Key Lab Med Image Comp & Comp Assisted I, Shanghai 200032, Peoples R China.
[6]Forschungszentrum Julich, Inst Neurosci & Med 4, D-52425 Julich, Germany.
[7]Sichuan Univ, West China Hosp, Dept Cardiovasc Surg, Chengdu 610041, Sichuan, Peoples R China.
(8.0+极速模式)