# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
###### Abstract  
A trial fibrillation (AF) is characterized by totally disorganized atrial depolarizations without effective atrial contraction. It is the most common form of cardiac arrhythmia, affecting more than 46.3 million people worldwide and its incidence rate remains increasing. Although AF itself is not life-threatening, its complications, such as strokes and heart failure, are lethal. About 25% of paroxysmal AF (PAF) patients become chronic for an observation period of more than one year. For long-term and real-time monitoring, a PAF prediction system was developed with four objectives: (1) high prediction accuracy, (2) fast computation, (3) small data storage, and (4) easy medical interpretations. The system takes a 400-point heart rate variability (HRV) sequence containing no AF episodes as the input and outputs whether the corresponding subject will experience AF episodes in the near future (i.e., 30 min). It first converts an input HRV sequence into four image matrices via extended Poincare plots to capture inter- and intra-person features. Then, the system employs a convolutional neural network (CNN) to perform feature selection and classification based on the input image matrices. Some design issues of the system, including feature conversion and classifier structure, were formulated as a binary optimization problem, which was then solved via a genetic algorithm (GA). A numerical study involving 6085 400-point HRV sequences excerpted from three PhysioNet databases showed that the developed PAF prediction system achieved 87.9% and 87.2% accuracy on the validation and the testing datasets, respectively. The performance is competitive with that of the leading PAF prediction system in the literature, yet our system is much faster and more intensively tested. Furthermore, from the designed inter-person features, we found that PAF patients often possess lower (~60 beats/min) or higher (~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. The other findings warrant further investigations for their medical implications about the onset of PAF. paroxysmal atrial fibrillation; heart rate variability; poincare plot; convolutional neural network; genetic algorithm +
Footnote †: journal: Medical Engineering, Fu Jen Catholic University, New Taipei City 24205, Taiwan; [EMAIL_ADDRESS]  
Footnote †: journal: Medical Engineering, Fuepisode is self-terminating (\(\leq\)48 h), and it is usually treatable. On-time prediction of PAF can prevent its transformation into chronic AF, which leads to high morbidity and mortality rates [5]. Owing to the highly nonlinear and nonstationary nature of human heartbeat signals [6; 7], the prediction of PAF onset via a signal-prediction approach (i.e., by predicting further heartbeat signals based on the existing ones and checking for AF episodes) is impractical. Instead, a feature-classification approach is more commonly adopted, in which one set of heartbeat signals acquired from PAF patients that contain no AF episodes but are known to exhibit AF episodes in the near future (say, within 30 min) are to be distinguished from the other set acquired from healthy subjects. In such an approach, various features that improve signal separability, as well as computational efficiency, are first extracted from the heartbeat signals. Then, a classifier is designed to distinguish the two sets of signals. In the last few decades, many methods have been proposed for the feature-classification approach. According to the signal source, they can be roughly classified into two categories: electrocardiogram (ECG)-based or heart rate variability (HRV)-based methods.