Assessment of atrial fibrillation inducibility based on epicardial mapping signals.
10.7507/1001-5515.201910005
- Author:
Kaiyue HE
1
;
Cuiwei YANG
2
,
3
,
4
Author Information
1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China.
2. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China
3. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200093, P.R.China
4. Shanghai Engineering Research Center of Cardiac Electrophysiology, Shanghai 201318, P.R.China.
- Publication Type:Journal Article
- Keywords:
atrial fibrillation;
binary logistic regression;
electrophysiology;
epicardial mapping
- From:
Journal of Biomedical Engineering
2020;37(3):487-495
- CountryChina
- Language:Chinese
-
Abstract:
Atrial fibrillation (AF) is the most common arrhythmia in clinic, which can cause hemodynamic changes, heart failure and stroke, and seriously affect human life and health. As a self-promoting disease, the treatment of AF can become more and more difficult with the deterioration of the disease, and the early prediction and intervention of AF is the key to curbing the deterioration of the disease. Based on this, in this study, by controlling the dose of acetylcholine, we changed the AF vulnerability of five mongrel dogs and tried to assess it by analyzing the electrophysiology of atrial epicardium under different states of sinus rhythm. Here, indices from four aspects were proposed to study the atrial activation rule. They are the variability of atrial activation rhythm, the change of the earliest atrial activation, the change of atrial activation delay and the left-right atrial dyssynchrony. By using binary logistic regression analysis, multiple indices above were transformed into the AF inducibility, which were used to classify the signals during sinus rhythm. The sensitivity, specificity and accuracy of classification reached 85.7%, 95.8% and 91.7%, respectively. As the experimental results show, the proposed method has the ability to assess the AF vulnerability of atrium, which is of great clinical significance for the early prediction and intervention of AF.
