Table of Contents Author Guidelines Submit a Manuscript
Complexity
Volume 2017, Article ID 2163610, 13 pages
https://doi.org/10.1155/2017/2163610
Research Article

Combined Nonlinear Analysis of Atrial and Ventricular Series for Automated Screening of Atrial Fibrillation

1Research Group in Electronic, Biomedical and Telecommunication Engineering, University of Castilla-La Mancha, Ciudad Real, Spain
2BioMIT.org, Electronic Engineering Department, Universitat Politecnica de Valencia, Valencia, Spain

Correspondence should be addressed to Raúl Alcaraz; se.mlcu@zaracla.luar

Received 6 June 2017; Accepted 24 September 2017; Published 26 October 2017

Academic Editor: Enzo Pasquale Scilingo

Copyright © 2017 Juan Ródenas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. S. Chugh, G. A. Roth, R. F. Gillum, and G. A. Mensah, “Global burden of atrial fibrillation in developed and developing nations,” Global Heart, vol. 9, no. 1, pp. 113–119, 2014. View at Publisher · View at Google Scholar · View at Scopus
  2. R. B. Schnabel, X. Yin, P. Gona et al., “50 year trends in atrial fibrillation prevalence, incidence, risk factors, and mortality in the Framingham Heart Study: A cohort study,” The Lancet, vol. 386, no. 9989, pp. 154–162, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. M. Zoni-Berisso, F. Lercari, T. Carazza, and S. Domenicucci, “Epidemiology of atrial fbrillation: European perspective,” Journal of Clinical Epidemiology, vol. 6, no. 1, pp. 213–220, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. U. Nations, “World population ageing 2015,” tech. rep., United Nations, 2015.
  5. B. P. Krijthe, A. Kunst, E. J. Benjamin et al., “Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060,” European Heart Journal, vol. 34, no. 35, pp. 2746–2751, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Colilla, A. Crow, W. Petkun, D. E. Singer, T. Simon, and X. Liu, “Estimates of current and future incidence and prevalence of atrial fibrillation in the U.S. adult population,” American Journal of Cardiology, vol. 112, no. 8, pp. 1142–1147, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Menke, L. Lüthje, A. Kastrup, and J. Larsen, “Thromboembolism in Atrial Fibrillation,” American Journal of Cardiology, vol. 105, no. 4, pp. 502–510, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. C. W. Khoo, S. Krishnamoorthy, H. S. Lim, and G. Y. H. Lip, “Atrial fibrillation, arrhythmia burden and thrombogenesis,” International Journal of Cardiology, vol. 157, no. 3, pp. 318–323, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. U. Schotten, D. Dobrev, P. G. Platonov, H. Kottkamp, and G. Hindricks, “Current controversies in determining the main mechanisms of atrial fibrillation,” Journal of Internal Medicine, vol. 279, no. 5, pp. 428–438, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Ferrari, M. Bertini, C. Blomstrom-Lundqvist et al., “An update on atrial fibrillation in 2014: From pathophysiology to treatment,” International Journal of Cardiology, vol. 203, pp. 22–29, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. V. Fuster, L. E. Rydén, D. S. Cannom et al., “2011 ACCF/AHA/HRS focused updates incorporated into the ACC/AHA/ESC 2006 Guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines developed in partnership with the European Society of Cardiology and in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society,” Journal of the American College of Cardiology, vol. 57, no. 11, pp. e101–e198, 2011. View at Google Scholar · View at Scopus
  12. A. Sheikh, N. J. Patel, N. Nalluri et al., “Trends in Hospitalization for Atrial Fibrillation: Epidemiology, Cost, and Implications for the Future,” Progress in Cardiovascular Diseases, vol. 58, no. 2, pp. 105–116, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. D. R. Van Wagoner, J. P. Piccini, C. M. Albert et al., “Progress toward the prevention and treatment of atrial fibrillation: A summary of the Heart Rhythm Society Research Forum on the Treatment and Prevention of Atrial Fibrillation, Washington, DC, December 9-10, 2013,” Heart Rhythm, vol. 12, no. 1, pp. e5–e29, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. C. B. de Vos, R. Pisters, R. Nieuwlaat et al., “Progression From Paroxysmal to Persistent Atrial Fibrillation. Clinical Correlates and Prognosis,” Journal of the American College of Cardiology, vol. 55, no. 8, pp. 725–731, 2010. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Amara, C. Montagnier, S. Cheggour et al., “early detection and treatment of atrial arrhythmias alleviates the arrhythmic burden in paced patients: the SETAM study,” Pacing and Clinical Electrophysiology, vol. 40, no. 5, pp. 527–536, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. P. Stachon, I. Ahrens, T. Faber, C. Bode, and A. Zirlik, “Asymptomatic atrial fibrillation and risk of stroke,” Panminerva Medica, vol. 57, no. 4, pp. 211–215, 2015. View at Google Scholar · View at Scopus
  17. A. Haeberlin, L. Roten, M. Schilling et al., “Software-based detection of atrial fibrillation in long-term ECGs,” Heart Rhythm, vol. 11, no. 6, pp. 933–938, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. G. Y. H. Lip, T. D. Hunter, M. E. Quiroz, P. D. Ziegler, and M. P. Turakhia, “Atrial fibrillation diagnosis timing, ambulatory ECG monitoring utilization, and risk of recurrent stroke,” Circulation: Cardiovascular Quality and Outcomes, vol. 10, no. 1, Article ID e002864, 2017. View at Publisher · View at Google Scholar · View at Scopus
  19. S. Swiryn, M. V. Orlov, D. G. Benditt et al., “Clinical implications of brief device-detected atrial tachyarrhythmias in a cardiac rhythm management device population: results from the registry of atrial tachycardia and atrial fibrillation episodes,” Circulation, vol. 134, no. 16, pp. 1130–1140, 2016. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Zhou, H. Ding, W. Wu, and Y. Zhang, “A real-time Atrial fibrillation detection algorithm based on the instantaneous state of heart rate,” PLoS ONE, vol. 10, no. 9, Article ID 0136544, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Petrutiu, J. Ng, G. M. Nijm, H. Al-Angari, S. Swiryn, and A. V. Sahakian, “Atrial fibrillation and waveform characterization: A time domain perspective in the surface ECG,” IEEE Engineering in Medicine and Biology Magazine, vol. 25, no. 6, article no. 14, pp. 24–30, 2006. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Masè, M. Disertori, M. Marini, and F. Ravelli, “Characterization of rate and regularity of ventricular response during atrial tachyarrhythmias. Insight on atrial and nodal determinants,” Physiological Measurement, vol. 38, no. 5, pp. 800–818, 2017. View at Publisher · View at Google Scholar
  23. A. Petrenas, V. Marozas, and L. Sörnmo, “Low-complexity detection of atrial fibrillation in continuous long-term monitoring,” Computers in Biology and Medicine, vol. 65, pp. 184–191, 2015. View at Publisher · View at Google Scholar · View at Scopus
  24. R. C. S. Seet, P. A. Friedman, and A. A. Rabinstein, “Prolonged rhythm monitoring for the detection of occult paroxysmal atrial fibrillation in ischemic stroke of unknown cause,” Circulation, vol. 124, no. 4, pp. 477–486, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Ladavich and B. Ghoraani, “Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity,” Biomedical Signal Processing and Control, vol. 18, pp. 274–281, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. J. Ródenas, M. García, R. Alcaraz, and J. J. Rieta, “Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms,” Entropy, vol. 17, no. 9, pp. 6179–6199, 2015. View at Publisher · View at Google Scholar · View at Scopus
  27. M. García, J. Ródenas, R. Alcaraz, and J. J. Rieta, “Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation,” Computer Methods and Programs in Biomedicine, vol. 131, pp. 157–168, 2016. View at Publisher · View at Google Scholar · View at Scopus
  28. H. Pürerfellner, E. Pokushalov, S. Sarkar et al., “P-wave evidence as a method for improving algorithm to detect atrial fibrillation in insertable cardiac monitors,” Heart Rhythm, vol. 11, no. 9, pp. 1575–1583, 2014. View at Publisher · View at Google Scholar · View at Scopus
  29. X. Du, N. Rao, M. Qian et al., “A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters,” Annals of Noninvasive Electrocardiology, vol. 19, no. 3, pp. 217–225, 2014. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Petrėnas, L. Sörnmo, A. Lukoševičius, and V. Marozas, “Detection of occult paroxysmal atrial fibrillation,” Medical & Biological Engineering & Computing, vol. 53, no. 4, pp. 287–297, 2015. View at Publisher · View at Google Scholar
  31. A. L. Goldberger, L. A. Amaral, L. Glass et al., “PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.,” Circulation, vol. 101, no. 23, pp. E215–E220, 2000. View at Publisher · View at Google Scholar · View at Scopus
  32. D. E. Lake and J. R. Moorman, “Accurate estimation of entropy in very short physiological time series: The problem of atrial fibrillation detection in implanted ventricular devices,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 300, no. 1, pp. H319–H325, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. J. Lee, Y. Nam, D. D. McManus, and K. H. Chon, “Time-varying coherence function for atrial fibrillation detection,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 10, pp. 2783–2793, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. I. Dotsinsky and T. Stoyanov, “Optimization of bi-directional digital filtering for drift suppression in electrocardiogram signals,” Journal of Medical Engineering & Technology, vol. 28, no. 4, pp. 178–180, 2004. View at Publisher · View at Google Scholar · View at Scopus
  35. L. Sörnmo and P. Laguna, Biomedical Signal Processing in Cardiac and Neurological Applications, Elsevier Academic Press, 2005.
  36. A. Martínez, R. Alcaraz, and J. J. Rieta, “Application of the phasor transform for automatic delineation of single-lead ECG fiducial points,” Physiological Measurement, vol. 31, no. 11, pp. 1467–1485, 2010. View at Publisher · View at Google Scholar · View at Scopus
  37. S. Cabrera, E. Vallès, B. Benito et al., “Simple predictors for new onset atrial fibrillation,” International Journal of Cardiology, vol. 221, pp. 515–520, 2016. View at Publisher · View at Google Scholar · View at Scopus
  38. R. Alcaraz and J. J. Rieta, “The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation,” Physiological Measurement, vol. 31, no. 1, pp. 115–130, 2010. View at Publisher · View at Google Scholar · View at Scopus
  39. R. Alcaraz and J. J. Rieta, “Review: Application of non-linear methods in the study of atrial fibrillation organization,” Journal of Medical and Biological Engineering, vol. 33, no. 3, pp. 239–252, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. V. Jacquemet, B. Dube, R. Nadeau et al., “Extraction and analysis of T waves in electrocardiograms during atrial flutter,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 1104–1112, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. A. A. Fossa and M. Zhou, “Assessing QT prolongation and electrocardiography restitution using a beat-to-beat method,” Cardiology Journal, vol. 17, no. 3, pp. 230–243, 2010. View at Google Scholar
  42. J. S. Richman and J. R. Moorman, “Physiological time-series analysis using approximate entropy and sample entropy,” American Journal of Physiology. Heart and Circulatory Physiology, vol. 278, pp. H2039–H2049, Jun 2000. View at Google Scholar
  43. R. Alcaraz, D. Abásolo, R. Hornero, and J. J. Rieta, “Optimal parameters study for sample entropy-based atrial fibrillation organization analysis,” Computer Methods and Programs in Biomedicine, vol. 99, no. 1, pp. 124–132, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. S. M. Pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 88, no. 6, pp. 2297–2301, 1991. View at Publisher · View at Google Scholar · View at Scopus
  45. D. E. Lake, “Renyi entropy measures of heart rate Gaussianity,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 1, pp. 21–27, 2006. View at Publisher · View at Google Scholar · View at Scopus
  46. K. Tateno and L. Glass, “Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and ΔRR intervals,” Medical & Biological Engineering & Computing, vol. 39, no. 6, pp. 664–671, 2001. View at Publisher · View at Google Scholar
  47. 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. View at Publisher · View at Google Scholar · View at Scopus
  48. C. Huang, S. Ye, H. Chen, D. Li, F. He, and Y. Tu, “A novel method for detection of the transition between atrial fibrillation and sinus rhythm,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 1113–1119, 2011. View at Publisher · View at Google Scholar · View at Scopus
  49. J. Lian, L. Wang, and D. Muessig, “A simple method to detect atrial fibrillation using RR intervals,” American Journal of Cardiology, vol. 107, no. 10, pp. 1494–1497, 2011. View at Publisher · View at Google Scholar · View at Scopus
  50. X. Zhou, H. Ding, B. Ung, E. Pickwell-MacPherson, and Y. Zhang, “Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy,” Biomedical Engineering Online, vol. 13, no. 1, article no. 18, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. S. Babaeizadeh, R. E. Gregg, E. D. Helfenbein, J. M. Lindauer, and S. H. Zhou, “Improvements in atrial fibrillation detection for real-time monitoring,” Journal of Electrocardiology, vol. 42, no. 6, pp. 522–526, 2009. View at Publisher · View at Google Scholar · View at Scopus
  52. K. Jiang, C. Huang, S.-M. Ye, and H. Chen, “High accuracy in automatic detection of atrial fibrillation for Holter monitoring,” Journal of Zhejiang University SCIENCE B, vol. 13, no. 9, pp. 751–756, 2012. View at Publisher · View at Google Scholar · View at Scopus
  53. S. Asgari, A. Mehrnia, and M. Moussavi, “Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine,” Computers in Biology and Medicine, vol. 60, pp. 132–142, 2015. View at Publisher · View at Google Scholar · View at Scopus
  54. R. Alcaraz, F. Hornero, and J. J. Rieta, “Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation,” Medical Engineering & Physics, vol. 33, no. 5, pp. 597–603, 2011. View at Publisher · View at Google Scholar · View at Scopus
  55. J. Slocum, A. Sahakian, and S. Swiryn, “Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity,” Journal of Electrocardiology, vol. 25, no. 1, pp. 1–8, 1992. View at Publisher · View at Google Scholar · View at Scopus
  56. M. Carrara, L. Carozzi, T. J. Moss et al., “Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy,” Physiological Measurement, vol. 36, no. 9, article no. 1873, pp. 1873–1888, 2015. View at Publisher · View at Google Scholar · View at Scopus
  57. C. W. Israel, “The role of pacing mode in the development of atrial fibrillation,” Europace, vol. 8, no. 2, pp. 89–95, 2006. View at Publisher · View at Google Scholar · View at Scopus
  58. A. Baranchuk and A. Bayés de Luna, “The P-wave morphology: what does it tell us?” Herzschrittmachertherapie und Elektrophysiologie, vol. 26, no. 3, pp. 192–199, 2015. View at Publisher · View at Google Scholar · View at Scopus
  59. G. Manis, “Fast computation of approximate entropy,” Computer Methods and Programs in Biomedicine, vol. 91, no. 1, pp. 48–54, 2008. View at Publisher · View at Google Scholar · View at Scopus
  60. Y.-H. Pan, Y.-H. Wang, S.-F. Liang, and K.-T. Lee, “Fast computation of sample entropy and approximate entropy in biomedicine,” Computer Methods and Programs in Biomedicine, vol. 104, no. 3, pp. 382–396, 2011. View at Publisher · View at Google Scholar · View at Scopus
  61. S.-W. Chen and Y.-H. Chen, “Hardware design and implementation of a wavelet de-noising procedure for medical signal preprocessing,” Sensors, vol. 15, no. 10, pp. 26396–26414, 2015. View at Publisher · View at Google Scholar · View at Scopus
  62. J. A. Gutiérrez-Gnecchi, R. Morfin-Magaña, D. Lorias-Espinoza et al., “DSP-based arrhythmia classification using wavelet transform and probabilistic neural network,” Biomedical Signal Processing and Control, vol. 32, pp. 44–56, 2017. View at Publisher · View at Google Scholar · View at Scopus