Browse > Article
http://dx.doi.org/10.6109/jkiice.2012.16.5.1047

Atrial Fibrillation Pattern Analysis based on Symbolization and Information Entropy  

Cho, Ik-Sung (부산대학교)
Kwon, Hyeog-Soong (부산대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.16, no.5, 2012 , pp. 1047-1054 More about this Journal
Abstract
Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice, and its risk increases with age. Conventionally, the way of detecting AF was the time·frequency domain analysis of RR variability. However, the detection of ECG signal is difficult because of the low amplitude of the P wave and the corruption by the noise. Also, the time·frequency domain analysis of RR variability has disadvantage to get the details of irregular RR interval rhythm. In this study, we describe an atrial fibrillation pattern analysis based on symbolization and information entropy. We transformed RR interval data into symbolic sequence through differential partition, analyzed RR interval pattern, quantified the complexity through Shannon entropy and detected atrial fibrillation. The detection algorithm was tested using the threshold between 10ms and 100ms on two databases, namely the MIT-BIH Atrial Fibrillation Database.
Keywords
atrial fibrillation; RR interval; symbolization; information entropy; MIT-BIH database;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Bollmann, F. Lombardi, Electrocardiology of Atrial Fibrillation. Current Knowledge and Future Challenges, IEEE Engineering in Medicine and Biology Magazine, Vol. 25, No 6, 2006, pages 15-23.
2 Furberg C, Psaty B, Manolio T, Gardin J, Smith V, Rautaharju P: Prevalence of atrial fibrillation in elderly subjects. The American Journal of Cardiology 1994, 74(3):236-241.   DOI   ScienceOn
3 Aytemir, K., S. Aksoyek, A. Yildirir, N. Ozer, and A. Oto. Prediction of atrial fibrillation recurrence after cardioversion by P wave signal-averaged electrocardiography. Int. J. Cardiol. 70:15-21, 1999.   DOI   ScienceOn
4 Clavier, L., J. M. Boucher, R. Lepage, J. J. Blanc, and J. C. Cornily. Automatic P-wave analysis of patients prone to atrial fibrillation. Med. Biol. Eng. Comput. 40:63-71, 2002.   DOI   ScienceOn
5 Dotsinsky, I. Atrial wave detection algorithm for discovery of some rhythm abnormalities. Physiol. Meas. 28:595-610, 2007.   DOI   ScienceOn
6 Duverney, D., J. Gaspoz, V. Pichot, F. Roche, R. Brion, A. Antoniadis, and J. Barthe' le'my. High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals. Pacing Clin. Electrophysiol. 25:457-462, 2002.   DOI   ScienceOn
7 Sarkar, S., D. Ritscher, and R. Mehra. A detector for a chronic implantable atrial tachyarrhythmia monitor. IEEE Trans. Biomed. Eng. 55:1219-1224, 2008.   DOI   ScienceOn
8 Tateno, K., and L. Glass. A method for detection of atrial fibrillation using RR intervals. Comput. Cardiol. 27:391-394, 2000.
9 S. Dash, K. H. Chon, S. Lu and E. A. Raeder "Automatic Real Time Detection of Atrial Fibrillation," Annals of Biomedical Engineering, Vol. 37, No. 9, pp. 1701-1709 (2009).   DOI   ScienceOn
10 Tateno, K., and L. Glass. Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and RR intervals. Med. Biol. Eng. Comput. 39:664-671, 2001.   DOI   ScienceOn
11 Cysarz D. Bettermann H, Leeuwen PV, Entropics of short binary sequences in heart period dynamics, Am J Physiol, 2000, 278, H2163-H2172.
12 Kurths J. Voss A, Saparin P, Witt A. Klcincr HJ. and Wessel N. Quantitative analysis of heart rate variability. Chaos. 1995m 5 L 88-94.   DOI
13 Douglas EL, Joshua SR, Griffin MP, and Moorman JR. Sample entropy analysis of neonatal heart rate variability, Am J Physol Requl Integr comp Physiol, 2002, 283 : 780-797.