A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System

Abstract

Atrial 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 Poincaré 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 (≤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.

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

“…Among the reported limitations of this work is the significant time of training phase of the algorithm and the limited number of subjects under study. Another recent account of deep learning capabilities for AF arrhythmia detection is offered by the work of Mendez et al [71], where the high performance of convolutional neural networks (CNN) for image analysis is exploited by translating AF detection into a suitable problem using of 400-point HRV sequences transformed into four extended Poincaré plots that serve as the CNN (visual) feature matrix inputs. The final input matrix choice is supported by a genetic algorithm.…”

Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis

“…Each classification category comprises a total of 25 distinct files. This is a very well-known dataset used in a myriad of scientific works [49][50][51].…”