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Open AccessArticle

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

1
School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210000, China
2
Departments of Computer Science and Biology, Emory University, Atlanta, GA 30322, USA
3
Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 320, Taiwan
4
Industrial Management Department, National Taiwan University of Science and Technology, Taipei 100, Taiwan
5
Center for Dynamical Biomarkers, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA 02215, USA
*
Authors to whom correspondence should be addressed.
Entropy 2017, 19(12), 677; https://doi.org/10.3390/e19120677
Received: 10 October 2017 / Revised: 20 November 2017 / Accepted: 8 December 2017 / Published: 10 December 2017
(This article belongs to the Special Issue Information Theory Applied to Physiological Signals)
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. View Full-Text
Keywords: paroxysmal atrial fibrillation; RR-interval time series; symbolic sequence; information-based similarity index; ensemble model paroxysmal atrial fibrillation; RR-interval time series; symbolic sequence; information-based similarity index; ensemble model
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MDPI and ACS Style

Cui, X.; Chang, E.; Yang, W.-H.; Jiang, B.C.; Yang, A.C.; Peng, C.-K. Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach. Entropy 2017, 19, 677. https://doi.org/10.3390/e19120677

AMA Style

Cui X, Chang E, Yang W-H, Jiang BC, Yang AC, Peng C-K. Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach. Entropy. 2017; 19(12):677. https://doi.org/10.3390/e19120677

Chicago/Turabian Style

Cui, Xingran; Chang, Emily; Yang, Wen-Hung; Jiang, Bernard C.; Yang, Albert C.; Peng, Chung-Kang. 2017. "Automated Detection of Paroxysmal Atrial Fibrillation Using an Information-Based Similarity Approach" Entropy 19, no. 12: 677. https://doi.org/10.3390/e19120677

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