# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 5 Conclusions  
In this paper, an HRV-based PAF prediction system was developed. We showed that the proposed PAF prediction system achieved 87.2% accuracy using 400-point (~5 min) HRV sequences. Although the accuracy was equivalent to that of the leading system in the literature, our system was more intensively tested (three large databases containing 608, 400-point HRV sequences), and had a faster prediction speed (1.25 millisecond per 400-point HRV sequence) and smaller storage requirement (<1 KB per 400-point HRV sequence). In addition to the system performance, we utilized a discretized Poincare plot to represent the features in an HRV sequence; such features are easy to construct and, more importantly, their medical implications can be easily interpreted since they still possess a direct link to the original HRV sequence. Instead of investigating the relationship between two consecutive points in the conventional Poincare plot, we further explored the relationship between two points that are some distance apart using a generalized Poincare plot. We also used a generalized Poincare plot to represent features from the original HRV sequence and its mean-removed sequence. Such a manipulation allows our system to explore the data from two (i.e., inter-person and intra-person) different aspects. Consequently, from the inter-person features, we found that PAF patients often possess lower (~60 beats/min)  
Figure 8: Clustering result of 4414 non-PAF and 1671 PAF HRV sequences using their converted features. The symbols of TP, TN, FP, and FN denote the true positive, true negative, false positive, and false negative, respectively. The number behind each group represents the group index, whereas the number in parentheses indicates the number of sequences in the corresponding subgroup. or higher (\(\sim\)100 beats/min) heart rates than non-PAF subjects. On the other hand, from the intra-person features, we observed that PAF patients often exhibit smaller variations (\(\leq\)5 beats/min) in heart rate than non-PAF subjects, but they may experience short bursts of large heart rate changes sometimes, probably due to abnormal beats, such as premature atrial beats. Finally, we converted the Poincare plot-based features into images and employed a CNN to select the crucial ones so as to distinguish PAF from non-PAF HRV sequences. Although the CNN has been shown to exhibit powerful image classification capabilities, training a new CNN is very time-consuming, not only to compute the weights in all of the network connections, but also to determine many parameters for the network structure. To determine suitable parameters in feature conversion (lag between data points and the number of input images) and in CNN construction, we adopted a GA approach, which is famous for its capability of finding near-global optimization solutions. As a result, we successfully constructed a PAF prediction system whose performance is competitive with the leading PAF prediction system in the literature. The system requires only Poincare plot-based features and thus outperforms many existing systems in computation speed. Furthermore, we anticipate that such features can contribute to identifying key differences between PAF and non-PAF HRV sequences, which may thus help cardiologists to pinpoint PAF onset mechanisms and eventually facilitate AF prevention. Project administration, supervision, funding acquisition, resources, methodology, and manuscript review, J.-T.Y. ; conceptualization, methodology, analysis, validation, visualization, manuscript writing, review, and editing K.-S.L. ; data collection, preparation, analysis, validation, and manuscript review, M.-C.H. ; manuscript preparation, writing, editing, formal analysis, and validation, M.M.M. All authors have read and agreed to the published version of the manuscript. This work was supported by the Ministry of Science and Technology of Taiwan under grant No. MOST 109-2221-E-030-017 and MOST 109-2113-M-030-004-MY2. Not applicable. The authors declare no conflict of interest.