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Signal Analysis in Atrial Fibrillation

  • Raúl AlcarazEmail author
  • José J. Rieta
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

Recent advances and clinical applications of signal analysis in the characterization of the most common supra-ventricular arrhythmia, i.e. atrial fibrillation (AF), are summarized in this chapter. The analysis of invasive and non-invasive electrocardiographic signals has revealed useful clinical information in a broad variety of scenarios, thus opening new perspectives in the understanding of the currently unknown mechanisms triggering and maintaining the arrhythmia.

Notes

Acknowledgements

This work has been supported by grants DPI2017–83952–C3 MINECO/AEI/FEDER, UE and SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha.

References

  1. 1.
    Chugh SS, Roth GA, Gillum RF et al (2014) Global burden of atrial fibrillation in developed and developing nations. Glob Heart 9(1):113–9CrossRefGoogle Scholar
  2. 2.
    Zoni-Berisso M, Lercari F, Carazza T et al (2014) Epidemiology of atrial fibrillation: European perspective. Clin Epidemiol 6:213–20CrossRefGoogle Scholar
  3. 3.
    Khoo CW, Krishnamoorthy S, Lim HS et al (2012) Atrial fibrillation, arrhythmia burden and thrombogenesis. Int J Cardiol 157(3):318–23CrossRefGoogle Scholar
  4. 4.
    Wodchis WP, Bhatia RS, Leblanc K et al (2012) A review of the cost of atrial fibrillation. Value Health 15(2):240–8CrossRefGoogle Scholar
  5. 5.
    Van Wagoner DR, Piccini JP (2013) Albert CM (2015) 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. Heart Rhythm 12(1):e5–e29CrossRefGoogle Scholar
  6. 6.
    Schotten U, Dobrev D, Platonov PG et al (2016) Current controversies in determining the main mechanisms of atrial fibrillation. J Intern Med 279(5):428–38CrossRefGoogle Scholar
  7. 7.
    Fabritz L, Guasch E, Antoniades C (2016) Expert consensus document: defining the major health modifiers causing atrial fibrillation: a roadmap to underpin personalized prevention and treatment. Nat Rev Cardiol 13(4):230–7CrossRefGoogle Scholar
  8. 8.
    January CT, Wann LS, Alpert JS et al (2014) 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society. J Am Coll Cardiol 64(21):e1–76CrossRefGoogle Scholar
  9. 9.
    Margulescu AD, Mont L (2017) Persistent atrial fibrillation vs paroxysmal atrial fibrillation: differences in management. Expert Rev Cardiovasc Ther 15(8):601–618CrossRefGoogle Scholar
  10. 10.
    Kirchhof P, Benussi S, Kotecha D et al (2016) 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J 37(38):2893–2962Google Scholar
  11. 11.
    Bond R, Olshansky B, Kirchhof P (2017) Recent advances in rhythm control for atrial fibrillation. F1000Res 6:1796CrossRefGoogle Scholar
  12. 12.
    Albåge A, Johansson B, Kennebäck G et al (2016) Long-term follow-up of cardiac rhythm in 320 patients after the Cox-Maze III procedure for atrial fibrillation. Ann Thorac Surg 101(4):1443–9CrossRefGoogle Scholar
  13. 13.
    Petrutiu S, Ng J, Nijm GM et al (2006) Atrial fibrillation and waveform characterization. a time domain perspective in the surface ECG. IEEE Eng Med Biol Mag 25:24–30CrossRefGoogle Scholar
  14. 14.
    Sörnmo L, Laguna P (2005) Bioelectrical signal processing in cardiac and neurological applications. Elsevier Academic PressGoogle Scholar
  15. 15.
    Sörnmo L, Stridh M, Husser D et al (2009) (1887) Analysis of atrial fibrillation: from electrocardiogram signal processing to clinical management. Philos Trans A Math Phys Eng Sci 367:235–53zbMATHCrossRefGoogle Scholar
  16. 16.
    Alcaraz R, Rieta JJ (2010) A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms. Biomed Signal Process Control 5:1–14CrossRefGoogle Scholar
  17. 17.
    Zhang Y, Mazgalev TN (2004) Ventricular rate control during atrial fibrillation and AV node modifications: past, present, and future. Pacing Clin Electrophysiol 27(3):382–93CrossRefGoogle Scholar
  18. 18.
    Sassi R, Cerutti S, Lombardi F et al (2015) Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC working group and the European heart rhythm association co-endorsed by the Asia Pacific heart rhythm society. Europace 17(9):1341–53CrossRefGoogle Scholar
  19. 19.
    Merah M, Abdelmalik TA, Larbi BH (2015) R-peaks detection based on stationary wavelet transform. Comput Methods Programs Biomed 121(3):149–60CrossRefGoogle Scholar
  20. 20.
    Nabil D, Reguig F (2015) Ectopic beats detection and correction methods: a review. Biomed Signal Process Control 18:228–244CrossRefGoogle Scholar
  21. 21.
    Stevenson WG, Soejima K (2005) Recording techniques for clinical electrophysiology. J Cardiovasc Electrophysiol 16(9):1017–22CrossRefGoogle Scholar
  22. 22.
    Martínez-Iniesta M, Ródenas J, Alcaraz R et al (2017) Waveform integrity in atrial fibrillation: The forgotten issue of cardiac electrophysiology. Ann Biomed Eng 45(8):1890–1907CrossRefGoogle Scholar
  23. 23.
    Rieta JJ, Hornero F (2007) Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation. Physiol Meas 28(8):925–36CrossRefGoogle Scholar
  24. 24.
    Corino VDA, Rivolta MW, Sassi R et al (2013) Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power. Med Eng Phys 35(12):1770–7CrossRefGoogle Scholar
  25. 25.
    Verheule S, Tuyls E, van Hunnik A et al (2010) Fibrillatory conduction in the atrial free walls of goats in persistent and permanent atrial fibrillation. Circ Arrhythm Electrophysiol 3(6):590–9CrossRefGoogle Scholar
  26. 26.
    Ng J, Sehgal V, Ng JK et al (2014) Iterative method to detect atrial activations and measure cycle length from electrograms during atrial fibrillation. IEEE Trans Biomed Eng 61(2):273–8CrossRefGoogle Scholar
  27. 27.
    Cantwell CD, Roney CH, Ng FS et al (2015) Techniques for automated local activation time annotation and conduction velocity estimation in cardiac mapping. Comput Biol Med 65:229–42CrossRefGoogle Scholar
  28. 28.
    Osorio D, Alcaraz R, Rieta J (2017) A fractionation-based local activation wave detector for atrial electrograms of atrial fibrillation. In: Computing in Cardiology, vol 44, pp 1–4Google Scholar
  29. 29.
    Gadenz L, Hashemi J, Shariat M et al (2017) Clinical role of dominant frequency measurements in atrial fibrillation ablation–a systematic review. J. Atr. Fibrillation 9(6):1–7Google Scholar
  30. 30.
    Bollmann A, Kanuru NK, McTeague KK et al (1998) Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide. Am J Cardiol 81(12):1439–1445CrossRefGoogle Scholar
  31. 31.
    Niwano S, Fukaya H, Sasaki T et al (2007) Effect of oral l-type calcium channel blocker on repetitive paroxysmal atrial fibrillation: spectral analysis of fibrillation waves in the holter monitoring. Europace 9(12):1209–15CrossRefGoogle Scholar
  32. 32.
    Alcaraz R, Rieta JJ (2009) Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Comput Methods Progr Biomed 93(2):148–154CrossRefGoogle Scholar
  33. 33.
    Petrutiu S, Sahakian AV, Swiryn S (2007) Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. Europace 9(7):466–70CrossRefGoogle Scholar
  34. 34.
    Nilsson F, Stridh M, Bollmann A et al (2006) Predicting spontaneous termination of atrial fibrillation using the surface ECG. Med Eng Phys 28(8):802–8CrossRefGoogle Scholar
  35. 35.
    Husser D, Cannom DS, Bhandari AK et al (2007) Electrocardiographic characteristics of fibrillatory waves in new-onset atrial fibrillation. Europace 9(8):638–42CrossRefGoogle Scholar
  36. 36.
    Bollmann A, Mende M, Neugebauer A et al (2002) Atrial fibrillatory frequency predicts atrial defibrillation threshold and early arrhythmia recurrence in patients undergoing internal cardioversion of persistent atrial fibrillation. Pacing Clin Electrophysiol 25(8):1179–84CrossRefGoogle Scholar
  37. 37.
    Bollmann A, Husser D, Steinert R et al (2003) Echocardiographic and electrocardiographic predictors for atrial fibrillation recurrence following cardioversion. J Cardiovasc Electrophysiol 14(10 Suppl):S162–S165CrossRefGoogle Scholar
  38. 38.
    Yoshida K, Chugh A, Good E et al (2010) A critical decrease in dominant frequency and clinical outcome after catheter ablation of persistent atrial fibrillation. Heart Rhythm 7(3):295–302CrossRefGoogle Scholar
  39. 39.
    Matsuo S, Lellouche N, Wright M et al (2009) Clinical predictors of termination and clinical outcome of catheter ablation for persistent atrial fibrillation. J Am Coll Cardiol 54(9):788–95CrossRefGoogle Scholar
  40. 40.
    Raine D, Langley P, Murray A et al (2005) Surface atrial frequency analysis in patients with atrial fibrillation: assessing the effects of linear left atrial ablation. J Cardiovasc Electrophysiol 16(8):838–44CrossRefGoogle Scholar
  41. 41.
    Garibaldi M, Zarzoso V, Latcu DG et al (2012) Predicting catheter ablation outcome in persistent atrial fibrillation using atrial dominant frequency and related spectral features. Conf Proc IEEE Eng Med Biol Soc 2012:613–6Google Scholar
  42. 42.
    Alcaraz R, Hornero F, Rieta JJ (2016) Electrocardiographic spectral features for long-term outcome prognosis of atrial fibrillation catheter ablation. Ann Biomed Eng 44(11):3307–3318CrossRefGoogle Scholar
  43. 43.
    Al Abed A, Guo T, Lovell NH et al (2013) Optimisation of ionic models to fit tissue action potentials: application to 3D atrial modelling. Comput Math Methods Med 2013:951234MathSciNetzbMATHCrossRefGoogle Scholar
  44. 44.
    Vayá C, Rieta JJ (2009) Time and frequency series combination for non-invasive regularity analysis of atrial fibrillation. Med Biol Eng Comput 47(7):687–96CrossRefGoogle Scholar
  45. 45.
    Stridh M, Sörnmo L, Meurling CJ et al (2004) Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis. IEEE Trans Biomed Eng 51(1):100–14CrossRefGoogle Scholar
  46. 46.
    Ortigosa N, Fernández C, Galbis A et al (2016) Classification of persistent and long-standing persistent atrial fibrillation by means of surface electrocardiograms. Biomed Tech (Berl) 61(1):19–27CrossRefGoogle Scholar
  47. 47.
    Alcaraz R, Rieta JJ (2012) Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings. Biomed Eng Online 11:46CrossRefGoogle Scholar
  48. 48.
    Sterling M, Huang DT, Ghoraani B (2014) Developing time-frequency features for prediction of the recurrence of atrial fibrillation after electrical cardioversion therapy. Conf Proc IEEE Eng Med Biol Soc 2014:5498–501Google Scholar
  49. 49.
    Alcaraz R, Hornero F, Martínez A et al (2012) Short-time regularity assessment of fibrillatory waves from the surface ECG in atrial fibrillation. Physiol Meas 33(6):969–84CrossRefGoogle Scholar
  50. 50.
    Alcaraz R, Rieta JJ, Hornero F (2009) Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion. Med Biol Eng Comput 47(12):1247–55CrossRefGoogle Scholar
  51. 51.
    Kao T, Su Y, Lu C et al (2005) Differentiation of atrial flutter and atrial fibrillation from surface electrocardiogram using nonlinear analysis. J Med Biolog Eng 25(3):117–122Google Scholar
  52. 52.
    Sun R, Wang Y (2008) Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot. Med Eng Phys 30(9):1105–11CrossRefGoogle Scholar
  53. 53.
    Baykaner T, Trikha R, Zaman JAB et al (2017) Electrocardiographic spatial loops indicate organization of atrial fibrillation minutes before ablation-related transitions to atrial tachycardia. J Electrocardiol 50(3):307–315CrossRefGoogle Scholar
  54. 54.
    Masè M, Disertori M, Marini M et al (2017) Characterization of rate and regularity of ventricular response during atrial tachyarrhythmias insight on atrial and nodal determinants. Physiol Meas 38(5):800–818CrossRefGoogle Scholar
  55. 55.
    Shaffer F, Ginsberg JP (2017) An overview of heart rate variability metrics and norms. Front Public Health 5:258CrossRefGoogle Scholar
  56. 56.
    Boon K, Khalil-Hani M, Malarvili M (2018) Paroxysmal atrial fibrillation prediction based on HRV analysis and non-dominated sorting genetic algorithm. Comput Methods Progr Biomed 153:171–184CrossRefGoogle Scholar
  57. 57.
    Bollmann A, Lombardi F (2006) Electrocardiology of atrial fibrillation. Current knowledge and future challenges. IEEE Eng Med Biol Mag 25(6):15–23CrossRefGoogle Scholar
  58. 58.
    Tuzcu V, Nas S, Börklü T et al (2006) Decrease in the heart rate complexity prior to the onset of atrial fibrillation. Europace 8(6):398–402CrossRefGoogle Scholar
  59. 59.
    Shin DG, Yoo CS, Yi SH et al (2006) Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation. Circ J 70(1):94–9CrossRefGoogle Scholar
  60. 60.
    Huang JH, Lin YK, Hsieh MH et al (2017) Modulation of autonomic nervous activity in the termination of paroxysmal atrial fibrillation. Pacing Clin Electrophysiol 40(4):401–408CrossRefGoogle Scholar
  61. 61.
    Choi W, Choi E, Piccirillo G et al (2014) Pre-cardioversion heart rate variability predicts recurrence of atrial fibrillation after electrical cardioversion. Exp Clin Cardiol 20(8):4419–4431Google Scholar
  62. 62.
    Bertaglia E, Zoppo F, Bonanno C et al (2005) Autonomic modulation of the sinus node following electrical cardioversion of persistent atrial fibrillation: relation with early recurrence. Int J Cardiol 102(2):219–23CrossRefGoogle Scholar
  63. 63.
    Mori H, Kato R, Ikeda Y, et al (2017) Analysis of the heart rate variability during cryoballoon ablation of atrial fibrillation. EuropaceGoogle Scholar
  64. 64.
    Seaborn GEJ, Todd K, Michael KA et al (2014) Heart rate variability and procedural outcome in catheter ablation for atrial fibrillation. Ann Noninvasive Electrocardiol 19(1):23–33CrossRefGoogle Scholar
  65. 65.
    Wongcharoen W, Kiatkumpol C, Phromminitikul A et al (2014) The predictive effort of heart rate variability on atrial fibrillation after coronary artery bypass grafting. Exp Clinical Cardiol 20(64):145–159Google Scholar
  66. 66.
    Compostella L, Russo N, D’Onofrio A et al (2015) Abnormal heart rate variability and atrial fibrillation after aortic surgery. Rev Bras Cir Cardiovasc 30(1):55–62Google Scholar
  67. 67.
    Mandel-Portnoy Y, Levin MA, Bansilal S et al (2016) Low intraoperative heart rate volatility is associated with early postoperative mortality in general surgical patients: a retrospective case-control study. J Clin Monit Comput 30(6):911–918CrossRefGoogle Scholar
  68. 68.
    Lombardi F, Tarricone D, Tundo F et al (2004) Autonomic nervous system and paroxysmal atrial fibrillation: a study based on the analysis of RR interval changes before, during and after paroxysmal atrial fibrillation. Eur Heart J 25(14):1242–8CrossRefGoogle Scholar
  69. 69.
    Sun RR, Wang YY (2009) Predicting spontaneous termination of atrial fibrillation based on the RR interval. Proc Inst Mech Eng H 223(6):713–26CrossRefGoogle Scholar
  70. 70.
    Stein KM, Borer JS, Hochreiter C et al (1994) Variability of the ventricular response in atrial fibrillation and prognosis in chronic nonischemic mitral regurgitation. Am J Cardiol 74(9):906–11CrossRefGoogle Scholar
  71. 71.
    Frey B, Heinz G, Binder T et al (1995) Diurnal variation of ventricular response to atrial fibrillation in patients with advanced heart failure. Am Heart J 129(1):58–65CrossRefGoogle Scholar
  72. 72.
    Platonov PG, Holmqvist F (2011) Atrial fibrillatory rate and irregularity of ventricular response as predictors of clinical outcome in patients with atrial fibrillation. J Electrocardiol 44(6):673–7CrossRefGoogle Scholar
  73. 73.
    Hayano J, Ishihara S, Fukuta H et al (2002) Circadian rhythm of atrioventricular conduction predicts long-term survival in patients with chronic atrial fibrillation. Chronobiol Int 19(3):633–48CrossRefGoogle Scholar
  74. 74.
    Oka T, Nakatsu T, Kusachi S et al (1998) Double-sector Lorenz plot scattering in an R-R interval analysis of patients with chronic atrial fibrillation: incidence and characteristics of vertices of the double-sector scattering. J Electrocardiol 31(3):227–35CrossRefGoogle Scholar
  75. 75.
    Climent AM, Guillem MS, Husser D et al (2009) Poincaré surface profiles of RR intervals: a novel noninvasive method for the evaluation of preferential AV nodal conduction during atrial fibrillation. IEEE Trans Biomed Eng 56(2):433–42CrossRefGoogle Scholar
  76. 76.
    Gelzer AR, Moïse NS, Vaidya D et al (2000) Temporal organization of atrial activity and irregular ventricular rhythm during spontaneous atrial fibrillation: an in vivo study in the horse. J Cardiovasc Electrophysiol 11(7):773–84CrossRefGoogle Scholar
  77. 77.
    Corino VDA, Ulimoen SR, Enger S et al (2015) Rate-control drugs affect variability and irregularity measures of RR intervals in patients with permanent atrial fibrillation. J Cardiovasc Electrophysiol 26(2):137–41CrossRefGoogle Scholar
  78. 78.
    Corino VDA, Holmqvist F, Mainardi LT et al (2014) Beta-blockade and A1-adenosine receptor agonist effects on atrial fibrillatory rate and atrioventricular conduction in patients with atrial fibrillation. Europace 16:587–94CrossRefGoogle Scholar
  79. 79.
    Climent AM, Atienza F, Millet J et al (2011) Generation of realistic atrial to atrial interval series during atrial fibrillation. Med Biol Eng Comput 49(11):1261–8CrossRefGoogle Scholar
  80. 80.
    Corino VDA, Sandberg F, Mainardi LT et al (2011) An atrioventricular node model for analysis of the ventricular response during atrial fibrillation. IEEE Trans Biomed Eng 58(12):3386–95CrossRefGoogle Scholar
  81. 81.
    Alcaraz R, Rieta JJ (2013) Nonlinear synchronization assessment between atrial and ventricular activations series from the surface ECG in atrial fibrillation. Biomed Signal Process Control 8(6)CrossRefGoogle Scholar
  82. 82.
    Mase M, Glass L, Disertori M et al (2013) The AV shynchrogram: a novel approacth to quantify atrioventricular couplin during atrial arrhythmias. Biomed Signal Process Control 8(6):1008–1016CrossRefGoogle Scholar
  83. 83.
    Masè M, Marini M, Disertori M et al (2015) Dynamics of AV coupling during human atrial fibrillation: role of atrial rate. Am J Physiol Heart Circ Physiol 309(1):H198–205CrossRefGoogle Scholar
  84. 84.
    Wells JL Jr, Karp RB, Kouchoukos NT et al (1978) Characterization of atrial fibrillation in man: studies following open heart surgery. Pacing Clin Electrophysiol 1:426–38CrossRefGoogle Scholar
  85. 85.
    Gaita F, Calò L, Riccardi R et al (2001) Different patterns of atrial activation in idiopathic atrial fibrillation: simultaneous multisite atrial mapping in patients with paroxysmal and chronic atrial fibrillation. J Am Coll Cardiol 37(2):534–41CrossRefGoogle Scholar
  86. 86.
    Hoekstra BP, Diks CG, Allessie MA et al (1995) Nonlinear analysis of epicardial atrial electrograms of electrically induced atrial fibrillation in man. J Cardiovasc Electrophysiol 6(6):419–40CrossRefGoogle Scholar
  87. 87.
    Mainardi LT, Porta A, Calcagnini G et al (2001) Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes. Med Biol Eng Comput 39(2):249–54CrossRefGoogle Scholar
  88. 88.
    Hoekstra BPT, Diks CGH, Allessie MA et al (1997) Nonlinear analysis of the pharmacological conversion of sustained atrial fibrillation in conscious goats by the class Ic drug cibenzoline. Chaos 7(3):430–446zbMATHCrossRefGoogle Scholar
  89. 89.
    Mainardi LT, Corino VDA, Lombardi L et al (2004) Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration. Biomed Eng Online 3(1):37CrossRefGoogle Scholar
  90. 90.
    Cirugeda-Rodán E, Novak D, Kremen V et al (2015) Caracterization of complex fractionated atrial electrograms by sample entropy: an international multi-center study. Entropy 17(11):7493–7509CrossRefGoogle Scholar
  91. 91.
    Orozco-Duque A, Novak D, Kremen V et al (2015) Multifractal analysis for grading complex fractionated electrograms in atrial fibrillation. Physiol Meas 36(11):2269–84CrossRefGoogle Scholar
  92. 92.
    Pitschner HF, Berkovic A, Grumbrecht S et al (1998) Multielectrode basket catheter mapping for human atrial fibrillation. J Cardiovasc Electrophysiol 9(8 Suppl):S48–56Google Scholar
  93. 93.
    Cervigón R, Moreno J, Reilly RB et al (2010) Entropy measurements in paroxysmal and persistent atrial fibrillation. Physiol Meas 31(7):1011–20CrossRefGoogle Scholar
  94. 94.
    Berkowitsch A, Carlsson J, Erdogan A et al (2000) Electrophysiological heterogeneity of atrial fibrillation and local effect of propafenone in the human right atrium: analysis based on symbolic dynamics. J Interv Card Electrophysiol 4(2):383–94CrossRefGoogle Scholar
  95. 95.
    Cervigón R, Moreno J, Sánchez C et al (2009) Atrial fibrillation organization: quantification of propofol effects. Med Biol Eng Comput 47(3):333–41CrossRefGoogle Scholar
  96. 96.
    Masè M, Faes L, Antolini R et al (2005) Quantification of synchronization during atrial fibrillation by Shannon entropy: validation in patients and computer model of atrial arrhythmias. Physiol Meas 26(6):911–23CrossRefGoogle Scholar
  97. 97.
    Mainardi LT, Corino VDA, Lombardi L et al (2006) Linear and nonlinear coupling between atrial signals. Three methods for the analysis of the relationships among atrial electrical activities in different sites. IEEE Eng Med Biol Mag 25(6):63–70CrossRefGoogle Scholar
  98. 98.
    Corino VDA, Mantica M, Lombardi F et al (2006) Assessment of spatial organization in the atria during paroxysmal atrial fibrillation and adrenergic stimulation. Biomed Tech (Berl) 51(4):260–3CrossRefGoogle Scholar
  99. 99.
    Yaksh A, Kik C, Knops P et al (2014) Atrial fibrillation: to map or not to map? Neth Heart J 22(6):259–66Google Scholar
  100. 100.
    Kim D, Ahn H (2012) Current stuatus and future cardiac mapping in atrial fibrillation. In: Choi PJI (ed) Atrial fibrillation—basic research and clinical applications, chap. 6. InTech, pp. 93–124Google Scholar
  101. 101.
    Narayan SM, Vishwanathan MN, Kowalewski CAB, et al (2017) The continuous challenge of af ablation: From foci to rotational activity. Rev Port Cardiol 36(Suppl 1):9–17CrossRefGoogle Scholar
  102. 102.
    Haissaguerre M, Hocini M, Denis A et al (2014) Driver domains in persistent atrial fibrillation. Circulation 130(7):530–8CrossRefGoogle Scholar
  103. 103.
    Atienza F, Climent AM, Guillem MS (2015) Frontiers in non-invasive cardiac mapping: rotors in atrial fibrillation-body surface frequency-phase mapping. Card Electrophysiol Clin 7(1):59–69CrossRefGoogle Scholar
  104. 104.
    Zhou Z, Jin Q, Chen LY et al (2016) Noninvasive imaging of high-frequency drivers and reconstruction of global dominant frequency maps in patients with paroxysmal and persistent atrial fibrillation. IEEE Trans Biomed Eng 63(6):1333–1340CrossRefGoogle Scholar
  105. 105.
    Rodrigo M, Guillem MS, Climent AM et al (2014) Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: a clinical-computational study. Heart Rhythm 11(9):1584–91CrossRefGoogle Scholar
  106. 106.
    King B, Porta-Sánchez A, Massé S et al (2017) Effect of spatial resolution and filtering on mapping cardiac fibrillation. Heart Rhythm 14(4):608–615CrossRefGoogle Scholar
  107. 107.
    Nattel S, Xiong F, Aguilar M (2017) Demystifying rotors and their place in clinical translation of atrial fibrillation mechanisms. Nat Rev Cardiol 14(9):509–520CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Research Group in Electronic, Biomedical and Telecommunication EngineeringUniversity of Castilla-La ManchaCuencaSpain
  2. 2.BioMIT.org, Electronic Engineering DepartmentUniversidad Politécnica de ValenciaValenciaSpain

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