Primary Study on Predicting the Termination of Paroxysmal Atrial Fibrillation Based on a Novel RdR RR Intervals Scatter Plot.
- Author:
Hongwei LU
;
Chenxi ZHANG
;
Ying SUN
;
Zhidong HAO
;
Chunfang WANG
;
Jiajia TIAN
- Publication Type:Journal Article
- MeSH:
Atrial Fibrillation;
diagnosis;
Computer Systems;
Heart Rate;
Humans;
Machine Learning
- From:
Journal of Biomedical Engineering
2015;32(4):763-766
- CountryChina
- Language:Chinese
-
Abstract:
Predicting the termination of paroxysmal atrial fibrillation (AF) may provide a signal to decide whether there is a need to intervene the AF timely. We proposed a novel RdR RR intervals scatter plot in our study. The abscissa of the RdR scatter plot was set to RR intervals and the ordinate was set as the difference between successive RR intervals. The RdR scatter plot includes information of RR intervals and difference between successive RR intervals, which captures more heart rate variability (HRV) information. By RdR scatter plot analysis of one minute RR intervals for 50 segments with non-terminating AF and immediately terminating AF, it was found that the points in RdR scatter plot of non-terminating AF were more decentralized than the ones of immediately terminating AF. By dividing the RdR scatter plot into uniform grids and counting the number of non-empty grids, non-terminating AF and immediately terminating AF segments were differentiated. By utilizing 49 RR intervals, for 20 segments of learning set, 17 segments were correctly detected, and for 30 segments of test set, 20 segments were detected. While utilizing 66 RR intervals, for 18 segments of learning set, 16 segments were correctly detected, and for 28 segments of test set, 20 segments were detected. The results demonstrated that during the last one minute before the termination of paroxysmal AF, the variance of the RR intervals and the difference of the neighboring two RR intervals became smaller. The termination of paroxysmal AF could be successfully predicted by utilizing the RdR scatter plot, while the predicting accuracy should be further improved.
