Detection of Atrial Fibrillation

  • Leif Sörnmo
  • Andrius Petrėnas
  • Vaidotas Marozas
Chapter
Part of the Series in BioEngineering book series (SERBIOENG)

Abstract

In this chapter, the main design principles used in detection of atrial fibrillation are reviewed, either exploring rhythm information only or information on both rhythm and atrial wave morphology. Aspects on detector implementation are briefly considered, and the pros and cons of different detection performance measures are discussed.

References

  1. 1.
    A. Haeberlin, L. Roten, M. Schilling, F. Scarcia, T. Niederhauser, R. Vogel, J. Fuhrer, H. Tanner, Software-based detection of atrial fibrillation in long-term ECGs. Heart Rhythm 11, 933–938 (2014)Google Scholar
  2. 2.
    K.M. Stein, J. Walden, N. Lippman, B.B. Lerman, Ventricular response in atrial fibrillation: random or deterministic? Am. J. Physiol. 277, H452–458 (1999)Google Scholar
  3. 3.
    J. Hayano, F. Yamasaki, S. Sakata, A. Okada, S. Mukai, T. Fujinami, Spectral characteristics of ventricular response to atrial fibrillation. Am. J. Physiol. 273, H2811–2816 (1997)Google Scholar
  4. 4.
    V. Fuster, L.E. Rydén, D.S. Cannom, H.J. Crijns, A.B. Curtis et al., ACC/AHA/ESC 2006 guidelines 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 European Society of Cardiology Committee for Practice Guidelines developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society Heart Association Task Force on practice guidelines and the European Society of Cardiology Committee for Practice Guidelines developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Europace 8, 651–745 (2006)Google Scholar
  5. 5.
    C.T. January, L.S. Wann, J.S. Alpert, H. Calkins, J.E. Cigarroa et al., 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. Circulation 130, 2071–2104 (2014)Google Scholar
  6. 6.
    P. Kirchhof, S. Benussi, D. Kotecha, A. Ahlsson, D. Atar, B. Casadei, M. Castella, H.C. Diener, H. Heidbuchel, J. Hendriks, G. Hindricks, A.S. Manolis, J. Oldgren, B.A. Popescu, U. Schotten, B. Van Putte, P. Vardas, S. Agewall, J. Camm, G. Baron Esquivias, W. Budts, S. Carerj, F. Casselman, A. Coca, R. De Caterina, S. Deftereos, D. Dobrev, J.M. Ferro, G. Filippatos, D. Fitzsimons, B. Gorenek, M. Guenoun, S.H. Hohnloser, P. Kolh, G.Y. Lip, A. Manolis, J. McMurray, P. Ponikowski, R. Rosenhek, F. Ruschitzka, I. Savelieva, S. Sharma, P. Suwalski, J.L. Tamargo, C.J. Taylor, I.C. Van Gelder, A.A. Voors, S. Windecker, J.L. Zamorano, K. Zeppenfeld, 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur. Heart J. 37, 2893–2962 (2016)Google Scholar
  7. 7.
    R.C.S. Seet, P.A. Friedman, A.A. Rabinstein, Prolonged rhythm monitoring for the detection of occult paroxysmal atrial fibrillation in ischemic stroke of unknown cause. Circulation 124, 477–486 (2011)Google Scholar
  8. 8.
    J.W. Keach, S.M. Bradley, M.P. Turakhia, T.M. Maddox, Early detection of occult atrial fibrillation and stroke prevention. Heart 101, 1097–1102 (2015)Google Scholar
  9. 9.
    J.G. Andrade, T. Field, P. Khairy, Detection of occult atrial fibrillation in patients with embolic stroke of uncertain source: a work in progress. Front. Physiol. 1, 1–9 (2015)Google Scholar
  10. 10.
    D.J. Miller, K. Shah, S. Modi, A. Mahajan, S. Zahoor, M. Affan, The evolution and application of cardiac monitoring for occult atrial fibrillation in cryptogenic stroke and TIA. Curr. Treat. Options Neurol. 18, 17 (2016)Google Scholar
  11. 11.
    J.O. Cerasuolo, L.E. Cipriano, L.A. Sposato, The complexity of atrial fibrillation newly diagnosed after ischemic stroke and transient ischemic attack: advances and uncertainties. Curr. Opin. Neurol. 30, 28–37 (2017)Google Scholar
  12. 12.
    A.H. Tayal, M. Tian, K.M. Kelly, S.C. Jones, D.G. Wright, D. Singh, J. Jarouse, J. Brillman, S. Murali, R. Gupta, Atrial fibrillation detected by mobile cardiac outpatient telemetry in cryptogenic TIA or stroke. Neurology 71, 1696–1701 (2008)Google Scholar
  13. 13.
    A.A. Rabinstein, J.E. Fugate, J. Mandrekar, J.D. Burns, R.C. Seet, S.A. Dupont, T.J. Kauffman, S.J. Asirvatham, P.A. Friedman, Paroxysmal atrial fibrillation in cryptogenic stroke: a case control study. J. Stroke Cerebrovascular Dis. 22, 1405–1411 (2013)Google Scholar
  14. 14.
    A.H. Abdul-Rahim, K.R. Lees, Paroxysmal atrial fibrillation after ischemic stroke: how should we hunt for it? Expert Rev. Cardiovasc. Ther. 11, 485–494 (2013)Google Scholar
  15. 15.
    C.G. Favilla, E. Ingala, J. Jara, E. Fessler, B. Cucchiara, S.R. Messé, M.T. Mullen, A. Prasad, J. Siegler, M.D. Hutchinson, S.E. Kasner, Predictors of finding occult atrial fibrillation after cryptogenic stroke. Stroke 46, 1210–1215 (2015)Google Scholar
  16. 16.
    E.I. Charitos, U. Stierle, P.D. Ziegler, M. Baldewig, D.R. Robinson, H. Sievers, T. Hanke, A comprehensive evaluation of rhythm monitoring strategies for the detection of atrial fibrillation recurrence: insights from 647 continuously monitored patients and implications for monitoring after therapeutic interventions. Circulation 126, 806–814 (2012)Google Scholar
  17. 17.
    T. Etgen, M. Hochreiter, M. Mundel, T. Freudenberger, Insertable cardiac event recorder in detection of atrial fibrillation after cryptogenic stroke: an audit report. Stroke 44, 2007–2009 (2013)Google Scholar
  18. 18.
    J. Reiffel, A. Verma, J.L. Halperin, B. Gersh, S. Tombul, J. Carrithers, L. Sherfesee, P. Kowey, Rationale and design of REVEAL AF: a prospective study of previously undiagnosed atrial fibrillation as documented by an insertable cardiac monitor in high-risk patients. Am. Heart J. 167, 22–27 (2014)Google Scholar
  19. 19.
    A.C. Flint, N.M. Banki, X. Ren, V.A. Rao, A.S. Go, Detection of paroxysmal atrial fibrillation by 30-day event monitoring in cryptogenic ischemic stroke: The stroke and monitoring for PAF in real time (SMART) registry. Stroke 43, 2788–2790 (2012)Google Scholar
  20. 20.
    S.B. Silverman, Paroxysmal atrial fibrillation: Novel strategies for monitoring and implications for treatment in stroke. Curr. Treat. Options Cardio. Med. 18, 1–13 (2016)Google Scholar
  21. 21.
    L. Sörnmo, P. Laguna, Bioelectrical Signal Processing in Cardiac and Neurological Applications (Elsevier (Academic Press), Amsterdam, 2005)Google Scholar
  22. 22.
    N. Lowres, L. Neubeck, J. Redfern, S.B. Freedman, Screening to identify unknown atrial fibrillation. A systematic review. Thromb. Haemost. 110, 213–222 (2013)Google Scholar
  23. 23.
    B. Vaes, S. Stalpaert, K. Tavernier, B. Thaels, D. Lapeire, W. Mullens, J. Degryse, The diagnostic accuracy of the MyDiagnostick to detect atrial fibrillation in primary care. BMC Fam. Pract. 15, 113 (2014)Google Scholar
  24. 24.
    F. Kaasenbrood, M.H.F.H. Rutten, L.J. Gerhards, A.W. Hoes, R.G. Tieleman, Yield of screening for atrial fibrillation in primary care with a hand-held, single-lead electrocardiogram device during influenza vaccination. Europace 18, 1514–1520 (2016)Google Scholar
  25. 25.
    L. Desteghe, Z. Raymaekers, M. Lutin, J. Vijgen, D. Dilling-Boer, P. Koopman, J. Schurmans, P. Vanduynhoven, P. Dendale, H. Heidbuchel, Performance of handheld electrocardiogram devices to detect atrial fibrillation in a cardiology and geriatric ward setting. Europace 19, 29–39 (2017)Google Scholar
  26. 26.
    E. Svennberg, J. Engdahl, F. Al-Khalili, L. Friberg, V. Frykman, M. Rosenqvist, Mass screening for untreated atrial fibrillation: the STROKESTOP study. Circulation 131, 2176–2184 (2015)Google Scholar
  27. 27.
    E. Svennberg, M. Stridh, J. Engdahl, F. Al-Khalili, L. Friberg, V. Frykman, M. Rosenquist, Safe automatic one-lead electrocardiogram analysis in screening for atrial fibrillation. Europace 19, 1449–1453 (2016)Google Scholar
  28. 28.
    S.R. Steinhubl, R.R. Mehta, G.S. Ebner, M.M. Ballesteros, J. Waalen, G. Steinberg, P. Van Crocker, Jr., E. Felicione, C. T. Carter, S. Edmonds, J. P. Honcz, G. D. Miralles, D. Talantov, T. C. Sarich, E. J. Topol, Rationale and design of a home-based trial using wearable sensors to detect asymptomatic atrial fibrillation in a targeted population: the mHealth screening to prevent strokes (mSToPS) trial. Am. Heart J. 175, 77–85 (2016)Google Scholar
  29. 29.
    M.P. Turakhia, D.W. Kaiser, Transforming the care of atrial fibrillation with mobile health. J. Interv. Card. Electrophysiol. 47, 45–50 (2016)Google Scholar
  30. 30.
    G. D. Clifford, C. Liu, B. Moody, L.-W. H. Lehman, I. Silva, Q. Li, A. Johnson, and R. G. Mark, “AF classification from a short single lead ECG recording: the PhysioNet Computing in Cardiology Challenge 2017, in Proceedings of Computing in Cardiology, vol. 44 (2017)Google Scholar
  31. 31.
    K. Tateno, L. Glass, Automatic detection of atrial fibrillation using the coefficient of variation and density histograms of RR and deltaRR intervals. Med. Biol. Eng. Comput. 39, 664–671 (2001)Google Scholar
  32. 32.
    S. Dash, K.H. Chon, S. Lu, E.A. Raeder, Automatic real time detection of atrial fibrillation. Ann. Biomed. Eng. 37, 1701–1709 (2009)Google Scholar
  33. 33.
    J. Lian, L. Wang, D. Muessig, A simple method to detect atrial fibrillation using RR intervals. Am. J. Cardiol. 107, 1494–1497 (2011)Google Scholar
  34. 34.
    D. E. Lake, J.R. Moorman, Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. Am. J. Physiol. (Heart Circ. Physiol.) 300: H319–H325 (2011)Google Scholar
  35. 35.
    C. Huang, S. Ye, H. Chen, D. Li, F. He, Y. Tu, A novel method for detection of the transition between atrial fibrillation and sinus rhythm. IEEE Trans. Biomed. Eng. 58, 1113–1119 (2011)Google Scholar
  36. 36.
    R.B. Shouldice, C. Heneghan, P. de Chazal, Automatic detection of paroxysmal atrial fibrillation, in Atrial fibrillation – basic research and clinical applications. (J. Choi, ed.), chap. 7, pp. 125–146, InTech (2012)Google Scholar
  37. 37.
    J. Lee, Y. Nam, D.D. McManus, K.H. Chon, Time-varying coherence function for atrial fibrillation detection. IEEE Trans. Biomed. Eng. 60, 2783–2793 (2013)Google Scholar
  38. 38.
    X. Zhou, H. Ding, B. Ung, E. Pickwell-MacPherson, Y. Zhang, Automatic online detection of atrial fibrillation based on symbolic dynamics and shannon entropy. Biomed. Eng. Online 13, 18 (2014)Google Scholar
  39. 39.
    S. Asgari, A. Mehrni, M. Moussavi, Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput. Biol. Med. 60, 132–142 (2015)Google Scholar
  40. 40.
    A. Petrėnas, V. Marozas, L. Sörnmo, Low-complexity detection of atrial fibrillation in continuous long-term monitoring. Comput. Biol. Med. 65, 184–191 (2015)Google Scholar
  41. 41.
    X. Zhou, H. Ding, W. Wu, Y. Zhang, A real-time atrial fibrillation detection algorithm based on the instantaneous state of heart rate. PLoS ONE 10, e0136544 (2015)Google Scholar
  42. 42.
    G.B. Moody, R.G. Mark, A new method for detecting atrial fibrillation using R-R intervals. in Proceedings of Computers in Cardiology vol. 10, pp. 227–230 (1983)Google Scholar
  43. 43.
    S. Cerutti, L.T. Mainardi, A. Porta, A.M. Bianchi, Analysis of the dynamics of RR interval series for the detection of atrial fibrillation episodes, in Proceedings of Computers in Cardiology, vol. 24, pp. 7–80 (1997)Google Scholar
  44. 44.
    S. Shkurovich, A.V. Sahakian, S. Swiryn, Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique. IEEE Trans. Biomed. Eng. 45, 229–234 (1998)Google Scholar
  45. 45.
    D. Duverney, J.M. Gaspoz, V. Pichot, F. Roche, R. Brion, A. Antoniadis, J.C. Barthelemy, High accuracy of automatic detection of atrial fibrillation using wavelet transform of heart rate intervals. Pacing Clin. Electrophysiol. 25, 457–462 (2002)Google Scholar
  46. 46.
    F. Yaghouby, A. Ayatollahi, R. Bahramali, M. Yaghouby, A.H. Alavi, Towards automatic detection of atrial fibrillation: a hybrid computational approach. Comput. Biol. Med. 40, 919–930 (2010)Google Scholar
  47. 47.
    C.-T. Lin, K.-C. Chang, C.-L. Lin, C.-C. Chiang, S.-W. Lu, S.-S. Chang, B.-S. Lin, H.-Y. Liang, R.-J. Chen, Y.-T. Lee, L.-W. Ko, An intelligent telecardiology system using a wearable and wireless ECG to detect atrial fibrillation. IEEE Trans. Info. Tech. Biomed. 14, 726–733 (2010)Google Scholar
  48. 48.
    P. Langley, M. Dewhurst, L.D. Marco, P. Adams, F. Dewhurst, J. Mwita, R. Walker, A. Murray, Accuracy of algorithms for detection of atrial fibrillation from short duration beat interval recordings. Med. Eng. Phys. 34, 1441–1447 (2012)Google Scholar
  49. 49.
    J. Lee, B. Reyes, D. McManus, O. Mathias, K. Chon, Atrial fibrillation detection using an iPhone 4S. IEEE Trans. Biomed. Eng. 60, 203–206 (2013)Google Scholar
  50. 50.
    J. Park, S. Lee, M. Jeon, Atrial fibrillation detection by heart rate variability in Poincaré plot. Biomed. Eng. Online 8, 1–12 (2009)Google Scholar
  51. 51.
    S. Sarkar, D. Ritscher, R. Mehra, A detector for a chronic implantable atrial tachyarrhythmia monitor. IEEE Trans. Biomed. Eng. 55, 1219–1224 (2008)Google Scholar
  52. 52.
    M.S. Kendall, A. Stuart, J.K. Ord, The Advanced Theory of Statistics, vol. 3, 4th edn. (High Wycombe: Charles Griffin, 1983)Google Scholar
  53. 53.
    S.B. Olsson, N. Cai, M. Dohnal, K.K. Talwar, Noninvasive support for and characterization of multiple intranodal pathways in patients with mitral valve disease and atrial fibrillation. Eur. Heart J. 7, 320–333 (1986)Google Scholar
  54. 54.
    N. Cai, M. Dohnal, S.B. Olsson, Methodological aspects of the use of heart rate stratified RR interval histograms in the analysis of atrioventricular conduction during atrial fibrillation. Cardiovasc. Res. 21, 455–462 (1987)Google Scholar
  55. 55.
    J. Dickinson Gibbons and S. Chakraborti, Nonparametric Statistical Inference, 5th edn. (Chapman and Hall/CRC, 2010)Google Scholar
  56. 56.
    J. Tebbenjohanns, B. Schumacher, T. Korte, M. Niehaus, D. Pfeiffer, Bimodal RR interval distribution in chronic atrial fibrillation: impact of dual atrioventricular nodal physiology on long-term rate control after catheter ablation of the posterior atrionodal input. J. Cardiovasc. Electrophysiol. 11, 497–503 (2000)Google Scholar
  57. 57.
    S. Rokas, S. Gaitanidou, S. Chatzidou, C. Pamboucas, D. Achtipis, S. Stamatelopoulos, Atrioventricular node modification in patients with chronic atrial fibrillation: role of morphology of RR interval variation. Circulation 103, 2942–2948 (2001)Google Scholar
  58. 58.
    V.D.A. Corino, F. Sandberg, L.T. Mainardi, L. Sörnmo, An atrioventricular node model for analysis of the ventricular response during atrial fibrillation. IEEE Trans. Biomed. Eng. 58, 3386–3395 (2011)Google Scholar
  59. 59.
    C.E. Shannon, A mathematical theory of communication. Bell Sys. Tech. J. 27, 379–423 (1948)MathSciNetMATHGoogle Scholar
  60. 60.
    S.J. Richman, J.R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. 278, H2039–H2049 (2000)Google Scholar
  61. 61.
    D.E. Lake, J.S. Richman, M.P. Griffin, J.R. Moorman, Sample entropy analysis of neonatal heart rate variability. Am. J. Physiol. Regul. Integr. Comp. Physiol. 283, R789–R797 (2002)Google Scholar
  62. 62.
    D.E. Lake, Renyi entropy measures of heart rate Gaussianity. IEEE Trans. Biomed. Eng. 53, 21–27 (2006)Google Scholar
  63. 63.
    M. S. Pincus, A.L. Goldberger, Physiological time-series analysis: what does regularity quantify? Am. J. Physiol. 266 (Heart Circ. Physiol.) 35: H1643–H1656 (1994)Google Scholar
  64. 64.
    W. Chen, Z. Wang, H. Xie, W. Yu, Characterization of surface EMG signal based on fuzzy entropy. IEEE Trans. Neural Sys. Rehab. Eng. 15, 266–272 (2007)Google Scholar
  65. 65.
    A. Avolio, Heart rate variability and stroke: strange attractors with loss of complexity. J. Hypertension 31, 1529–1531 (2013)Google Scholar
  66. 66.
    M. Julián, R. Alcaraz, J.J. Rieta, Comparative assessment of nonlinear metrics to quantify organization-related events in surface electrocardiograms of atrial fibrillation. Comput. Biol. Med. 48, 66–76 (2014)Google Scholar
  67. 67.
    L. Hong-wei, S. Ying, L. Min, L. Pi-ding, Z. Zheng, A probability density function method for detecting atrial fibrillation using R-R intervals. Med. Eng. Phys. 31, 116–123 (2009)Google Scholar
  68. 68.
    P. Grassberger, I. Procaccia, Characterization of strange attractors. Phys. Rev. Lett. 50, 346–349 (1983)MathSciNetMATHGoogle Scholar
  69. 69.
    T. Anan, K. Sunagawa, H. Araki, M. Nakamura, Arrhythmia analysis by successive RR plotting. J. Electrocardiol. 23, 243–248 (1990)Google Scholar
  70. 70.
    P.W. Kamen, H. Krum, A.M. Tonkin, Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin. Sci. (Lond.) 91, 201–208 (1996)Google Scholar
  71. 71.
    M. Brennan, M. Palaniswami, P. Kamen, Poincaré plot interpretation using a physiological model of HRV based on a network of oscillators. Am. J. Physiol. Heart Circ. Physiol. 283, H1873–H1886 (2002)Google Scholar
  72. 72.
    M. Malik, Standard measurements of heart rate variability, in Dynamic electrocardiography ed. by M. Malik, A.J. Camm, chap. 2, (Wiley–Blackwell, New York, 2004), pp. 13–21Google Scholar
  73. 73.
    K. Monahan, Y. Song, K. Loparo, R. Mehra, F.E. Harrell Jr., S. Redline, Automated detection of atrial fibrillation from the electrocardiogram channel of polysomnograms. Sleep Breath 20, 515–522 (2015)Google Scholar
  74. 74.
    C.K. Karmakar, A.H. Khandoker, J. Gubbi, M. Palaniswami, Complex correlation measure: a novel descriptor for Poincaré plot. BioMed. Eng. Online 8, 37–48 (2009)Google Scholar
  75. 75.
    L. Zhang, T. Guo, B. Xi, Y. Fan, K. Wang, J. Bi, Y. Wang, Automatic recognition of cardiac arrhythmias based on the geometric patterns of Poincaré plots. Physiol. Meas. 36, 283–301 (2015)Google Scholar
  76. 76.
    R. Mehra, J. Gillberg, P. Ziegler, S. Sarkar, Algorithms for atrial tachyarrhythmia detection for long-term monitoring with implantable devices, in Understanding atrial fibrillation: the signal processing contribution ed. by L.T. Mainardi, L.Sörnmo, S. Cerutti, chap. 8 (Morgan & Claypool, San Francisco, 2008), pp. 175–214Google Scholar
  77. 77.
    H. Käsmacher, S. Wiese, M. Lahl, Monitoring the complexity of ventricular response in atrial fibrillation. Discrete Dynamics Nature Soc. 4, 63–75 (2000)Google Scholar
  78. 78.
    M. Brennan, M. Palaniswami, P. Kamen, Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48, 1342–1347 (2001)Google Scholar
  79. 79.
    R.A. Thuraisingham, An electrocardiogram marker to detect paroxysmal atrial fibrillation. J. Electrocardiol. 40, 344–347 (2007)Google Scholar
  80. 80.
    H. Zhao, S. Lu, R. Zou, K. Ju, K.H. Chon, Estimation of time-varying coherence function using time-varying transfer functions. Ann. Biomed. Eng. 33, 1582–1594 (2005)Google Scholar
  81. 81.
    R. Zou, H. Wang, K.H. Chon, A robust time-varying identification algorithm using basis functions. Ann. Biomed. Eng 31, 840–853 (2003)Google Scholar
  82. 82.
    F. van der Heijden, R.P.W. Duin, D. de Ridder, D.M.J. Tax, Classification, Parameter Estimation and State Estimation–An Engineering Approach using Matlab (Wiley, New York, 2005)MATHGoogle Scholar
  83. 83.
    P. Carvalho, J. Henriques, R. Couceiro, M. Harris, M. Antunes, J. Habetha, Model-based atrial fibrillation detection, in ECG signal processing, classification and interpretation ed. by A. Gacek, W. Pedrycz (Springer London, 2012), pp. 99–133Google Scholar
  84. 84.
    R. Colloca, A.E.W. Johnson, L. Mainardi, G.D. Clifford, A support vector machine approach for reliable detection of atrial fibrillation events, in Proceedings of Computing in Cardiology, vol. 40, pp. 1047–1050 (2013)Google Scholar
  85. 85.
    R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd edn. (Wiley–Interscience, New York, 2001)Google Scholar
  86. 86.
    N. Larburu, T. Lopetegi, I. Romero, Comparative study of algorithms for atrial fibrillation detection, in Proceedings of Computing in Cardiology, vol. 38, pp. 265–268 (2011)Google Scholar
  87. 87.
    S. Babaeizadeh, R.E. Gregg, E.D. Helfenbein, J.M. Lindauer, S.H. Zhou, Improvements in atrial fibrillation detection for real-time monitoring. J. Electrocardiol. 42, 522–526 (2009)Google Scholar
  88. 88.
    S. Ladavich, B. Ghoraani, Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity. Biomed. Signal Process. Control 18, 274–281 (2015)Google Scholar
  89. 89.
    J. Ródenas, M. García, R. Alcaraz, J.J. Rieta, Wavelet entropy automatically detects episodes of atrial fibrillation from single-lead electrocardiograms. Entropy 17, 6179–6199 (2015)Google Scholar
  90. 90.
    Y. Xia, N. Wulan, K. Wang, H. Zhang, Detecting atrial fibrillation by deep convolutional neural networks. Comput. Biol. Med. 93, 84–92 (2018)Google Scholar
  91. 91.
    P. Laguna, R. Jané, P. Caminal, Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. Comput. Biomed. Res. 27, 45–60 (1994)Google Scholar
  92. 92.
    J.P. Martínez, R. Almeida, S. Olmos, A.P. Rocha, P. Laguna, A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans. Biomed. Eng. 51, 570–581 (2004)Google Scholar
  93. 93.
    J. Dumont, A. Hernández, G. Carrault, Improving ECG beats delineation with an evolutionary optimization process. IEEE Trans. Biomed. Eng. 57, 607–615 (2010)Google Scholar
  94. 94.
    L. Clavier, J.-M. Boucher, R. Lepage, J.-J. Blanc, J.-C. Cornily, Automatic P-wave analysis of patients prone to atrial fibrillation. Med. Biol. Eng. Comput. 40, 63–71 (2002)Google Scholar
  95. 95.
    F. Censi, G. Calcagnini, C. Ricci, R.P. Ricci, M. Santini, A. Grammatico, P. Bartolini, P-wave morphology assessment by a Gaussian functions-based model in atrial fibrillation patients. IEEE Trans. Biomed. Eng. 54, 663–671 (2007)Google Scholar
  96. 96.
    A. Martínez, D. Abásolo, R. Alcaraz, J.J. Rieta, Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation. Med. Eng. Phys. 37, 692–697 (2015)Google Scholar
  97. 97.
    P. Laguna, R. G. Mark, A. L. Goldberger, and G. B. Moody, “A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG, in Proceedings of Computers in Cardiology, Vol. 23, pp. 673–676 (1997)Google Scholar
  98. 98.
    A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.K. Peng, H.E. Stanley, PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215–220 (2000)Google Scholar
  99. 99.
    I. Dotsinsky, Atrial wave detection algorithm for discovery of some rhythm abnormalities. Physiol. Meas 28, 595–610 (2007)Google Scholar
  100. 100.
    A. Petrėnas, L. Sörnmo, A. Lukoševičius, V. Marozas, Detection of occult paroxysmal atrial fibrillation. Med. Biol. Eng. Comput. 53, 287–297 (2015)Google Scholar
  101. 101.
    A. Petrėnas, V. Marozas, L. Sörnmo, A. Lukoševičius, An echo state neural network for QRST cancellation during atrial fibrillation. IEEE Trans. Biomed. Eng. 59, 2950–2957 (2012)Google Scholar
  102. 102.
    S. Roberts, L. Tarassenko, A probabilistic resource allocating network for novelty detection. Neural Comput. 6, 270–284 (1994)Google Scholar
  103. 103.
    M. Markou, S. Singh, Novelty detection: a review. Signal Process. 83, 2481–2497 (2003)MATHGoogle Scholar
  104. 104.
    X. Du, N. Rao, M. Qian, D. Liu, J. Li, W. Feng, L. Yin, X. Chen, A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters. Ann. Noninvasive Electrocardiol. 19, 217–225 (2014)Google Scholar
  105. 105.
    F. Castells, J.J. Rieta, J. Millet, V. Zarzoso, Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias. IEEE Trans. Biomed. Eng. 52, 258–267 (2005)Google Scholar
  106. 106.
    R. Llinares, J. Igual, J. Miró-Borrás, A fixed point algorithm for extracting the atrial activity in the frequency domain. Comput. Biol. Med. 40, 943–949 (2010)Google Scholar
  107. 107.
    S.M. Kay, Modern Spectral Estimation Theory and Application (Prentice-Hall, New Jersey, 1999)Google Scholar
  108. 108.
    M. Stridh, A. Bollmann, S.B. Olsson, L. Sörnmo, Time-frequency analysis of atrial tachyarrhythmias: detection and feature extraction. IEEE Eng. Med. Biol. Mag. 25, 31–39 (2006)Google Scholar
  109. 109.
    Q. Li, R.G. Mark, G.D. Clifford, Robust heart rate estimation from multiple asynchronous noisy sources using signal quality indices and a Kalman filter. Physiol. Meas. 29, 15–32 (2008)Google Scholar
  110. 110.
    G.D. Clifford, D. Clifton, Wireless technology in disease management and medicine. Ann. Rev. Med. 63, 479–92 (2012)Google Scholar
  111. 111.
    J. Behar, J. Oster, Q. Li, G.D. Clifford, ECG signal quality during arrhythmia and its application to false alarm reduction. IEEE Trans. Biomed. Eng. 60, 1660–1666 (2013)Google Scholar
  112. 112.
    A.E.W. Johnson, J. Behar, F. Andreotti, G.D. Clifford, J. Oster, Multimodal heart beat detection using signal quality indices. Physiol. Meas. 36, 1665–1677 (2015)Google Scholar
  113. 113.
    C. Orphanidou, T. Bonnici, P. Charlton, D. Clifton, D. Vallance, L. Tarassenko, Signal-quality indices for the electrocardiogram and photoplethysmogram: derivation and applications to wireless monitoring. IEEE J. Biomed. Health Inform. 19, 832–838 (2015)Google Scholar
  114. 114.
    J. Oster, G.D. Clifford, Impact of the presence of noise on RR interval-based atrial fibrillation detection. J. Electrocardiol. 48, 947–951 (2015)Google Scholar
  115. 115.
    N. Gambarotta, F. Aletti, G. Baselli, M. Ferrario, A review of methods for the signal quality assessment to improve reliability of heart rate and blood pressures derived parameters. Med. Biol. Eng. Comput. 54, 1025–1035 (2016)Google Scholar
  116. 116.
    S.H. Rappaport, L. Gillick, G.B. Moody, R.G. Mark, QRS morphology classification: quantitative evaluation of different strategies, in Proceedings of Computers in Cardiology, Vol. 9, pp. 33–38 (1982)Google Scholar
  117. 117.
    M. Lagerholm, C. Peterson, G. Braccini, L. Edenbrandt, L. Sörnmo, Clustering ECG complexes using Hermite functions and self-organizing maps. IEEE Trans. Biomed. Eng. 47, 838–848 (2000)Google Scholar
  118. 118.
    P. de Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51, 1196–1206 (2004)Google Scholar
  119. 119.
    M. Llamedo, J.P. Martínez, Heartbeat classification using feature selection driven by database generalization criteria. IEEE Trans. Biomed. Eng. 58, 616–625 (2011)Google Scholar
  120. 120.
    J. Oster, J. Behar, O. Sayadi, S. Nemati, A.E.W. Johnson, G.D. Clifford, Semisupervised ECG ventricular beat classification with novelty detection based on switching Kalman filters. IEEE Trans. Biomed. Eng. 62, 2125–2134 (2015)Google Scholar
  121. 121.
    R. Couceiro, P. Carvalho, J. Henriques, M. Antunes, M. Harris, J. Habertha, Detection of atrial fibrillation using model-based ECG analysis, in Proceedings of International Conference on Pattern Recognition (ICPR), vol. 19, pp. 1–5 (2008)Google Scholar
  122. 122.
    T. Jeon, B. Kim, M. Jeon, B.-G. Lee, Implementation of a portable device for real-time ECG signal analysis. Biomed. Eng. Online 13, 1–13 (2014)Google Scholar
  123. 123.
    O. Andersson, K.H. Chon, L. Sörnmo, J. Neves Rodrigues, A 290 mV sub-V\({}_{{\rm T}}\) ASIC for real-time atrial fibrillation detection. IEEE Trans. Biomed. Circ. Sys. 9: 377–386 (2015)Google Scholar
  124. 124.
    B. Matthews, Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica et Biophysica Acta 405, 442–451 (1975)Google Scholar
  125. 125.
    P. Baldi, S. Brunak, Y. Chauvin, C.A. Andersen, H. Nielsen, Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424 (2000)Google Scholar
  126. 126.
    F. Jager, G.B. Moody, A. Taddei, R. Mark, Performance measures for algorithms to detect transient ischemic ST segment changes, in Proceedings of Computers in Cardiology, vol. 18, pp. 369–372 (1991)Google Scholar
  127. 127.
    R.J. Martis, U.R. Acharya, H. Prasad, C.K. Chua, C.M. Lim, Automated detection of atrial fibrillation using Bayesian paradigm. Knowledge-Based Syst. 54, 269–275 (2013)Google Scholar
  128. 128.
    M. García, J. Ródenas, R. Alcaraz, J.J. Rieta, Application of the relative wavelet energy to heart rate independent detection of atrial fibrillation. Comput. Meth. Progr. Biomed. 131, 157–168 (2016)Google Scholar
  129. 129.
    A. Isaksson, M. Wallman, H. Göransson, M.G. Gustafsson, Cross-validation and bootstrapping are unreliable in small sample classification. Pattern Recogn. Letter 29, 1960–1965 (2008)Google Scholar
  130. 130.
    J. Slocum, A.V. Sahakian, S. Swiryn, Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. J. Electrocardiol. 25, 1–8 (1992)Google Scholar
  131. 131.
    M. Haïssaguerre, P. Jaïs, D.C. Shah, A. Takahashi, M. Hocini, G. Quiniou, S. Garrigue, A. Le Mouroux, P. Le Métayer, J. Clémenty, Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. N. Engl. J. Med. 339, 659–666 (1998)Google Scholar
  132. 132.
    S.-A. Chen, M.-H. Hsieh, C.-T. Tai, C.-F. Tsai, V.S. Prakash, W.-C. Yu, T.-L. Hsu, Y.-A. Ding, M.-S. Chang, Initiation of atrial fibrillation by ectopic beats originating from the pulmonary veins: electrophysiological characteristics, pharmacological responses, and effects of radiofrequency ablation. Circulation 100, 1879–1886 (1999)Google Scholar
  133. 133.
    C. Kolb, S. Nürnberger, G. Ndrepepa, B. Zrenner, A. Schömig, C. Schmitt, Modes of initiation of paroxysmal atrial fibrillation from analysis of spontaneously occurring episodes using a 12-lead Holter monitoring system. Am. J. Cardiol. 88, 853–857 (2001)Google Scholar
  134. 134.
    D. Wallmann, D. Tüller, K. Wustmann, P. Meier, J. Isenegger, M. Arnold, H.P. Mattle, E. Delacrétaz, Frequent atrial premature beats predict paroxysmal atrial fibrillation in stroke patients. An opportunity for a new diagnostic strategy. Stroke 38, 2292–2294 (2007)Google Scholar
  135. 135.
    K.T. Nguyen, E. Vittinghoff, T.A. Dewland, M.C. Mandyam, P.K. Stein, E.Z. Soliman, S.R. Heckbert, G.M. Marcus, Electrocardiographic predictors of incident atrial fibrillation. Am. J. Cardiol. 118, 714–719 (2016)Google Scholar
  136. 136.
    T. Thong, J. McNames, M. Aboy, B. Goldstein, Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes. IEEE Trans. Biomed. Eng. 4, 561–569 (2004)Google Scholar
  137. 137.
    J.L. Huang, W.C. Wen, W.L. Lee, M.S. Chang, S.A. Chen, Changes of autonomic tone before the onset of paroxysmal atrial fibrillation. Int. J. Cardiol. 66, 275–283 (1998)Google Scholar
  138. 138.
    M. Bettoni, M. Zimmermann, Autonomic tone variations before the onset of paroxysmal atrial fibrillation. Circulation 105, 2753–2759 (2002)Google Scholar
  139. 139.
    C. Gallo, P.P. Bocchino, M. Magnano, L. Gaido, D. Zema, A. Battaglia, M. Anselmino, F. Gaita, Autonomic tone activity before the onset of atrial fibrillation. J. Cardiovasc. Electrophysiol. 28, 304–314 (2017)Google Scholar
  140. 140.
    F. Lombardi, D. Tarricone, F. Tundo, F. Colombo, S. Belletti, C. Fiorentini, 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, 1242–1248 (2004)Google Scholar
  141. 141.
    D.-G. Shin, C.-S. Yoo, S.-H. Yi, J.-H. Bae, Y.-J. Kim, J.-S. Park, G.-R. Hong, Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation. Circ. J. 70, 94–99 (2006)Google Scholar
  142. 142.
    Y. Chesnokov, Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods. Artif. Intell. Med. 43, 151–165 (2008)Google Scholar
  143. 143.
    M. Mohebbi, H. Ghassemian, Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal. Comput. Meth. Progr. Biomed. 5, 40–49 (2012)Google Scholar
  144. 144.
    F. Holmqvist, P.G. Platonov, J. Carlson, W. Zareba, A.J. Moss, Altered interatrial conduction detected in MADIT II patients bound to develop atrial fibrillation. Ann. Noninvasive Electrocardiol. 14:268–275 (2009)Google Scholar
  145. 145.
    F. Holmqvist, P.G. Platonov, S. McNitt, S. Polonsky, J. Carlson, W. Zareba, A.J. Moss, Abnormal P-wave morphology is a predictor of atrial fibrillation development and cardiac death in MADIT II patients. Ann. Noninvasive Electrocardiol. 15: 63–72 (2010)Google Scholar
  146. 146.
    H. Gonna, M.M. Gallagher, X.H. Guo, Y.G. Yap, K. Hnatkova, A.J. Camm, P-wave abnormality predicts recurrence of atrial fibrillation after electrical cardioversion: a prospective study. Ann. Noninvasive Electrocardiol. 19, 57–62 (2014)Google Scholar
  147. 147.
    J.B. Nielsen, J.T. Kühl, A. Pietersen, C. Graff, B. Lind, J.J. Struijk, M.S. Olesen, M.F. Sinner, T.N. Bachmann, S. Haunsø, B.G. Nordestgaard, P.T. Ellinor, J.H. Svendsen, K.F. Kofoed, L. Køber, A.G. Holst, P-wave duration and the risk of atrial fibrillation: results from the copenhagen ECG study. Heart Rhythm 12, 1887–1895 (2015)Google Scholar
  148. 148.
    G. Conte, A. Luca, S. Yazdani, M.L. Caputo, F. Regol, T. Moccetti, L. Kappenberger, J.-M. Vesin, A. Auricchio, Usefulness of P-wave duration and morphologic variability to identify patients prone to paroxysmal atrial fibrillation. Am. J. Cardiol. 119, 275–279 (2017)Google Scholar
  149. 149.
    I.C.Y. Chang, E. Austin, B. Krishnan, D.G. Benditt, C.N. Quay, L.H. Ling, L.Y. Chen, Shorter minimum P-wave duration is associated with paroxysmal lone atrial fibrillation. J. Electrocardiol. 47, 106–112 (2014)Google Scholar
  150. 150.
    F. Nilsson, M. Stridh, A. Bollmann, L. Sörnmo, Predicting spontaneous termination of atrial fibrillation using the surface ECG. Med. Eng. Phys. 26, 802–808 (2006)Google Scholar
  151. 151.
    P. G. Platonov, V.D.A. Corino, M. Seifert, F. Holmqvist, L. Sörnmo, Atrial fibrillatory rate in the clinical context: natural course and prediction of intervention outcome. Europace 16: iv110–iv119 (2014)Google Scholar
  152. 152.
    R. Alcaraz, J.J. Rieta, Application of wavelet entropy to predict atrial fibrillation progression from the surface ECG. Comput. Math. Meth. Med. 13, 1–9 (2012)MATHGoogle Scholar
  153. 153.
    T. Tanantong, E. Nantajeewarawat, S. Thiemjarus, False alarm reduction in BSN-based cardiac monitoring using signal quality and activity type information. Sensors 15, 3952–3974 (2015)Google Scholar
  154. 154.
    T. Koivisto, M. Pänkäälä, T. Hurnanen, T. Vasankari, T. Kiviniemi, A. Saraste, J. Airaksinen, Automatic detection of atrial fibrillation using MEMS accelerometer. in Proceedings of Computing in Cardiology, vol. 42, pp. 829–832 (2015)Google Scholar
  155. 155.
    C. Brueser, J. Diesel, M.D.H. Zink, S. Winter, P. Schauerte, S. Leonhardt, Automatic detection of atrial fibrillation in cardiac vibration signals. IEEE J. Biomed. Health Inform. 17, 162–171 (2013)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Leif Sörnmo
    • 1
  • Andrius Petrėnas
    • 2
  • Vaidotas Marozas
    • 2
  1. 1.Department of Biomedical Engineering and Center for Integrative ElectrocardiologyLund UniversityLundSweden
  2. 2.Biomedical Engineering InstituteKaunas University of TechnologyKaunasLithuania

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