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Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation

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Abstract

Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20 ms fixed resolution in y-axes and 15–60 s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen’s Kappa coefficients were 0.87, 0.91 and 0.64 at 50 s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60 s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40 s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.

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References

  1. Rieta JJ, Ravelli F, Sornmo L (2013) Advances in modeling and characterization of atrial arrhythmias. Biomed Signal Process 8(6):956–957

    Article  Google Scholar 

  2. Sposato LA, Cipriano LE, Saposnik G et al (2015) Diagnosis of atrial fibrillation after stroke and transient ischaemic attack: a systematic review and meta-analysis. Lancet Neurol 14(4):377–387

    Article  PubMed  Google Scholar 

  3. Taggar JS, Coleman T, Lewis S et al (2015) Accuracy of methods for diagnosing atrial fibrillation using 12-lead ECG: a systematic review and meta-analysis. Int J Cardiol 184:175–183

    Article  PubMed  Google Scholar 

  4. Huang C, Ye S, Chen H et al (2011) A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans Bio Med Eng 58(4):1113–1119

    Article  Google Scholar 

  5. German DM, Kabir MM, Dewland TA et al (2016) Atrial fibrillation predictors: importance of the electrocardiogram. Ann Noninvasive Electrocardiol 21(1):20–29

    Article  PubMed  Google Scholar 

  6. Petrenas A, Marozas V, Jarusevicius G et al (2015) A modified Lewis ECG lead system for ambulatory monitoring of atrial arrhythmias. J Electrocardiol 48:157–163

    Article  CAS  PubMed  Google Scholar 

  7. Okutucu S, Aytemir K, Oto A (2016) P-wave dispersion: what we know till now? JRSM Cardiovasc Dis 5:1–9

    Google Scholar 

  8. Martinez A, Alcaraz R, Rieta JJ (2014) Morphological variability of the P-wave for premature envision of paroxysmal atrial fibrillation events. Physiol Measurement 35:1–14

    Article  Google Scholar 

  9. Asgari S, Mehrni A, Moussavi M (2015) Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med 60:132–142

    Article  PubMed  Google Scholar 

  10. Ladavich S, Ghoraani B (2015) Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed Signal Process Control 8:274–281

    Article  Google Scholar 

  11. Du X, Rao N, Qian M et al (2014) A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann Noninvasive Electrocardiol 19(3):217–225

    Article  PubMed  Google Scholar 

  12. Rodenas J, García M, Alcaraz R et al (2015) Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy 17:6179–6199

    Article  Google Scholar 

  13. Garcia M, Rodenas J, Alcaraz R et al (2016) Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. Comput Methods Progr Biomed 131:157–168

    Article  Google Scholar 

  14. Lee J, Nam Y, McManus DD et al (2013) Time-varying coherence function for atrial fibrillation detection. IEEE Trans Bio Med Eng 60(10):2783–2793

    Article  Google Scholar 

  15. Nurul AA, Norlaili MS, Mohd AO (2016) Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system. Int J Cardiol 222:504–508

    Article  Google Scholar 

  16. Oster J, Clifford GD (2015) Impact of the presence of noise on RR interval-based atrial fibrillation detection. J Electrocardiol 48:947–951

    Article  PubMed  Google Scholar 

  17. Mishra A, Swati D (2015) The recursive combination filter approach of pre-processing for the estimation of standard deviation of RR series. Australas Phys Eng Sci Med 38:413–423

    Article  PubMed  Google Scholar 

  18. Marwaha P, Sunkaria RK (2016) Complexity quantification of cardiac variability time series using improved sample entropy (I-SampEn). Australas Phys Eng Sci Med. doi:10.1007/s13246-016-0457-7

    PubMed  Google Scholar 

  19. Petrenas A, Marozas V, Sornmo L (2015) Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Comput Biol Med 65:184–191

    Article  PubMed  Google Scholar 

  20. Islam MS, Ammour N, Alajlan N et al (2016) Rhythm-based heartbeat duration normalization for atrial fibrillation detection. Comput Biol Med 72:160–169

    Article  PubMed  Google Scholar 

  21. Dash S, Chon KH, Lu S et al (2009) Automatic real time detection of atrial fibrillation. Ann Biomed Eng 37(9):1701–1709

    Article  CAS  PubMed  Google Scholar 

  22. Goldberger AL, Amaral LAN, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220. http://www.physionet.org/physiobank/database/

  23. Lian J, Wang L, Muessig D (2011) A simple method to detect atrial fibrillation using RR intervals. AM J Cardiol 107(10):1494–1497

    Article  PubMed  Google Scholar 

  24. Tateno K, Glass L (2001) Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and delta RR intervals. Med Biol Eng Comput 39(6):664–671

    Article  CAS  PubMed  Google Scholar 

  25. Logan B, Healey J (2005) Robust detection of atrial fibrillation for a long term telemonitoring system. Comput Cardiol 32(1):619–622

    Google Scholar 

  26. Kikillus N, Hammer G, Lentz N et al (2007) Three different algorithms for identifying patients suffering from atrial fibrillation during atrial fibrillation free phases of the ECG. Comput Cardiol 34(1):801–804

    Google Scholar 

  27. Lake DE, Moorman JR (2011) Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. AM J Physiol Heart C 300(1):319–325

    Article  Google Scholar 

  28. Zhou X, Ding H, Ben J (2014) Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy. BioMed Eng Online 13:18. http://www.biomedical-engineering-online.com/content/13/1/18

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Acknowledgements

This study was funded by State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center (SMFA15B06, SMFA15A01), and it was also funded by China National Natural Science Fund (81471743, 81601561, 61401417).

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Authors and Affiliations

  1. School of Life Science, Beijing Institute of Technology, ZhongGuanCun South Rd 5#, Haidian, Beijing, China

    Yanjun Li, Xiaoying Tang & Ancong Wang

  2. State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, China

    Yanjun Li

  3. Hongfeng Control Company of the Sanjiang Space Group, Xiaogan, Hubei, China

    Hui Tang

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  1. Yanjun Li
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  2. Xiaoying Tang
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  3. Ancong Wang
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  4. Hui Tang
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Correspondence to Xiaoying Tang.

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The authors declare that there is no conflict of interest regarding the publication of this article.

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This article does not contain any studies with human or animal subjects performed by any of the authors.

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All data used in this paper are from the open databases of the PhysioNet “http://www.physionet.org/physiobank/database/”.

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Li, Y., Tang, X., Wang, A. et al. Probability density distribution of delta RR intervals: a novel method for the detection of atrial fibrillation. Australas Phys Eng Sci Med 40, 707–716 (2017). https://doi.org/10.1007/s13246-017-0554-2

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  • DOI: https://doi.org/10.1007/s13246-017-0554-2

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