Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach

Xingran Cui,Bernard Jiang,Emily Chang,Albert Yang,Chung-Kang Peng,Wen-Hung Yang

doi:10.3390/e19120677

Abstract

Atrial fibrillation (AF) is an abnormal rhythm of the heart, which can increase heart-related complications. Paroxysmal AF episodes occur intermittently with varying duration. Human-based diagnosis of paroxysmal AF with a longer-term electrocardiogram recording is time-consuming. Here we present a fully automated ensemble model for AF episode detection based on RR-interval time series, applying a novel approach of information-based similarity analysis and ensemble scheme. By mapping RR-interval time series to binary symbolic sequences and comparing the rank-frequency patterns of m-bit words, the dissimilarity between AF and normal sinus rhythms (NSR) were quantified. To achieve high detection specificity and sensitivity, and low variance, a weighted variation of bagging with multiple AF and NSR templates was applied. By performing dissimilarity comparisons between unknown RR-interval time series and multiple templates, paroxysmal AF episodes were detected. Based on our results, optimal AF detection parameters are symbolic word length m = 9 and observation window n = 150, achieving 97.04% sensitivity, 97.96% specificity, and 97.78% overall accuracy. Sensitivity, specificity, and overall accuracy vary little despite changes in m and n parameters. This study provides quantitative information to enhance the categorization of AF and normal cardiac rhythms.

Highlights

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is an abnormal heart rhythm characterized by rapid and irregular beating of the atria [1]
We have previously proposed a novel information-based similarity (IBS) index to detect and quantify the repetitive appearance of certain basic patterns that are embedded in the human heart rate time series using tools from physics and statistical linguistics [33,34,35,36]
The AF database consists of 25 ECG recordings (10 h in duration) of patients with Paroxysmal AF, the normal sinus rhythms (NSR) database consists of 18 long-term ECG recordings from healthy subjects who had no whereas the NSR database consists of 18 long-term ECG recordings from healthy subjects who had no significant arrhythmias [39,40,41]

Summary

Introduction

Atrial fibrillation (AF), the most common sustained cardiac arrhythmia, is an abnormal heart rhythm characterized by rapid and irregular beating of the atria [1]. AF (PAF), termed intermittent AF, is defined as an episode of AF that terminates spontaneously or with intervention in less than seven days [3]. The frequency of PAF is uncertain, because previous studies have suggested that a majority of these episodes are asymptomatic [4,5], including some that may last more than 48 h [4]. Entropy 2017, 19, 677 of the electrocardiogram (ECG) chart. Due to the paroxysmal nature of the onset and termination of PAF in certain patients, human-based diagnosis of AF is usually time consuming when using a longer-term ECG recording such as a Holter or event recorder. An automated, computerized AF detector may provide timely diagnosis and have substantial clinical utility

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