Databases and Simulation

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


The most popular public databases employed in engineering-oriented research are described in this chapter. Various aspects on the simulation of ECG signals in atrial fibrillation are considered, and a simulator of paroxysmal atrial fibrillation is described in detail. The chapter ends with a discussion of the relevance of simulation.


  1. 1.
    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)CrossRefGoogle Scholar
  2. 2.
    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, 227–230 (1983)Google Scholar
  3. 3.
    S. Petrutiu, A.V. Sahakian, S. Swiryn, Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. Europace 9, 466–470 (2007)CrossRefGoogle Scholar
  4. 4.
    G.B. Moody, Spontaneous termination of atrial fibrillation: a challenge from PhysioNet and Computers in Cardiology 2004, in Proceedings of Computers in Cardiology vol. 31, 101–104 (2004)Google Scholar
  5. 5.
    G.D. Clifford, C. Liu, B. Moody, L.-W.H. Lehman, I. Silva, Q. Li, A. Johnson, 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, 1 (2017)Google Scholar
  6. 6.
    M. Henriksson, A. Petrėnas, V. Marozas, F. Sandberg, L. Sörnmo, Model-based assessment of f-wave signal quality in patients with atrial fibrillation. IEEE Trans. Biomed. Eng. (2018, accepted)Google Scholar
  7. 7.
    R.G. Mark, P.S. Schluter, G.B. Moody, P.H. Devlin, D. Chernoff, An annotated ECG database for evaluating arrhythmia detectors. Proc. IEEE Front. Eng. Health Care, 205–210 (1982)Google Scholar
  8. 8.
    M. Stridh, L. Sörnmo, C.J. Meurling, S.B. Olsson, Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis. IEEE Trans. Biomed. Eng. 51, 100–114 (2004)CrossRefGoogle Scholar
  9. 9.
    M. Stridh, L. Sörnmo, Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Trans. Biomed. Eng. 48, 105–111 (2001)CrossRefGoogle Scholar
  10. 10.
    F. Sandberg, M. Stridh, L. Sörnmo, Robust time-frequency analysis of atrial fibrillation using hidden Markov models. IEEE Trans. Biomed. Eng. 55, 502–511 (2008)CrossRefGoogle Scholar
  11. 11.
    V.D.A. Corino, L.T. Mainardi, M. Stridh, L. Sörmno, Improved time-frequency analysis of atrial fibrillation signals using spectral modelling. IEEE Trans. Biomed. Eng. 56, 2723–2730 (2008)CrossRefGoogle Scholar
  12. 12.
    R. Alcaraz, J.J. Rieta, Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination. Comput. Biol. Med. 39, 697–706 (2009)CrossRefGoogle Scholar
  13. 13.
    R. Sassi, V.D.A. Corino, L.T. Mainardi, Analysis of surface atrial signals: time series with missing data? Ann. Biomed. Eng. 37, 2082–2092 (2009)CrossRefGoogle Scholar
  14. 14.
    H. Dai, S. Jiang, Y. Li, Atrial activity extraction from single lead ECG recordings: evaluation of two novel methods. Comput. Biol. Med. 43, 176–183 (2013)CrossRefGoogle Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    V. Jacquemet, A. van Oosterom, J.-M. Vesin, L. Kappenberger, Analysis of electrocardiograms during atrial fibrillation: a biophysical approach. IEEE Med. Biol. Eng. Mag. 25, 79–88 (2006)CrossRefGoogle Scholar
  18. 18.
    O. Blanc, N. Virag, J.-M. Vesin, L. Kappenberger, A computer model of human atria with reasonable computation load and realistic anatomical properties. IEEE Trans. Biomed. Eng. 48, 1229–1237 (2001)CrossRefGoogle Scholar
  19. 19.
    N. Virag, V. Jacquemet, C.S. Henriquez, S. Zozor, O. Blanc, J.-M. Vesin, E. Pruvot, L. Kappenberger, Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria. Chaos 12, 754–763 (2002)CrossRefGoogle Scholar
  20. 20.
    A. Petrėnas, V. Marozas, A. Sološenko, R. Kubilius, J. Skibarkienė, J. Oster, L. Sörnmo, Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes. Physiol. Meas. 38, 2058–2080 (2017)CrossRefGoogle Scholar
  21. 21.
    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)CrossRefGoogle Scholar
  22. 22.
    M.S. Guillem, A.M. Climent, J. Millet, Á. Arenal, F. Fernández-Avilés, J. Jalife, F. Atienza, O. Berenfeld, Noninvasive localization of maximal frequency sites of atrial fibrillation by body surface potential mapping. Circ. Arrhythm. Electrophysiol. 6, 294–301 (2013)CrossRefGoogle Scholar
  23. 23.
    F. Ravelli, M. Masè, M.D. Greco, L. Faes, M. Disertori, Deterioration of organization in the first minutes of atrial fibrillation: a beat-to-beat analysis of cycle length and wave similarity. J. Cardiovasc. Electrophysiol. 18, 60–65 (2007)CrossRefGoogle Scholar
  24. 24.
    R. Alcaraz, J.J. Rieta, Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Comput. Methods Programs Biomed. 93, 148–154 (2009)CrossRefGoogle Scholar
  25. 25.
    M. Masè, M. Marini, M. Disertori, F. Ravelli, Dynamics of AV coupling during human atrial fibrillation: role of atrial rate. Am. J. Physiol. Heart Circ. Physiol. 309, H198–H205 (2015)CrossRefGoogle Scholar
  26. 26.
    J. Malik, N. Reed, C.-L. Wang, H.-T. Wu, Single-lead f-wave extraction using diffusion geometry. Physiol. Meas. 38, 1310–1334 (2017)CrossRefGoogle Scholar
  27. 27.
    R. Sameni, G.D. Clifford, C. Jutten, M.B. Shamsollahi, Multichannel ECG and noise modeling: application to maternal and fetal ECG signals. J. Adv. Signal Process., 1–14 (2007)Google Scholar
  28. 28.
    P.E. McSharry, G.D. Clifford, L. Tarassenko, L.A. Smith, A dynamical model for generating synthetic electrocardiogram signals. IEEE Trans. Biomed. Eng. 50, 289–294 (2003)CrossRefGoogle Scholar
  29. 29.
    G.R. Pai, J.M. Rawles, The QT interval in atrial fibrillation. Brit. Heart J. 61, 510–513 (1989)CrossRefGoogle Scholar
  30. 30.
    D.L. Musat, M. Adhaduk, M.W. Preminger, A. Arshad, T. Sichrovsky, J.S. Steinberg, S. Mittal, Correlation of QT interval correction methods during atrial fibrillation and sinus rhythm. Am. J. Cardiol. 112, 1379–1383 (2013)CrossRefGoogle Scholar
  31. 31.
    L. Sörnmo, P.O. Börjesson, M.E. Nygårds, O. Pahlm, A method for evaluation of QRS shape features using a mathematical model for the ECG. IEEE Trans. Biomed. Eng. 28, 713–717 (1981)CrossRefGoogle Scholar
  32. 32.
    P. Laguna, R. Jané, S. Olmos, N.V. Thakor, H. Rix, P. Caminal, Adaptive estimation of QRS complex by the Hermite model for classification and ectopic beat detection. Med. Biol. Eng. Comput 34, 58–68 (1996)CrossRefGoogle Scholar
  33. 33.
    T.H. Linh, S. Osowski, M. Stodolski, On-line heart beat recognition using Hermite polynomials and neuro-fuzzy network. IEEE Trans. Instrum. Measure. 52, 1224–1231 (2003)CrossRefGoogle Scholar
  34. 34.
    H. Haraldsson, L. Edenbrandt, M. Ohlsson, Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks. Artif. Intell. Med. 32, 127–136 (2004)CrossRefGoogle Scholar
  35. 35.
    A. Sandryhaila, S. Saba, M. Puschel, J. Kovacevic, Efficient compression of QRS complexes using Hermite expansion. IEEE Trans. Signal Process. 60, 947–955 (2012)MathSciNetCrossRefGoogle Scholar
  36. 36.
    R. Havmöller, J. Carlson, F. Holmqvist, A. Herreros, C. Meurling, S.B. Olsson, P.G. Platonov, Age-related changes in P wave morphology in healthy subjects. BMC Cardiovasc. Disord. 7, 22 (2007)Google Scholar
  37. 37.
    F. Holmqvist, M.S. Olesen, A. Tveit, S. Enger, J. Tapanainen, R. Jurkko, R. Havmöller, S. Haunsø, J. Carlson, J.H. Svendsen, P.G. Platonov, Abnormal atrial activation in young patients with lone atrial fibrillation. Europace 13, 188–192 (2011)CrossRefGoogle Scholar
  38. 38.
    H.C. Bazett, An analysis of the time relations of electrocardiograms. Heart 7, 353–370 (1920)Google Scholar
  39. 39.
    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)CrossRefGoogle Scholar
  40. 40.
    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)CrossRefGoogle Scholar
  41. 41.
    M. Weber-Krüger, K. Gröschel, M. Mende, J. Seegers, R. Lahno, B. Haase, C.-F. Niehaus, F. Edelmann, G. Hasenfuß, R. Wachter, R. Stahrenberg, Excessive supraventricular ectopic activity is indicative of paroxysmal atrial fibrillation in patients with cerebral ischemia. PLoS ONE 8, e67602 (2013)CrossRefGoogle Scholar
  42. 42.
    D.J. Gladstone, P. Dorian, M. Spring, V. Panzov, M. Mamdani, J.S. Healey, K.E. Thorpe, for EMBRACE Steering Committee and Investigators, Atrial premature beats predict atrial fibrillation in cryptogenic stroke: results from the EMBRACE trial. Stroke 46, 936–941 (2015)Google Scholar
  43. 43.
    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)CrossRefGoogle Scholar
  44. 44.
    M. Åström, E. Carro, L. Sörnmo, P. Laguna, B. Wohlfart, Vectorcardiographic loop alignment and the measurement of morphologic beat-to-beat variability in noisy signals. IEEE Trans. Biomed. Eng. 47, 497–506 (2000)CrossRefGoogle Scholar
  45. 45.
    R. Bailón, L. Sörnmo, P. Laguna, A robust method for ECG-based estimation of the respiratory frequency during stress testing. IEEE Trans. Biomed. Eng. 53, 1273–1285 (2006)CrossRefGoogle Scholar
  46. 46.
    M.M. Platiša, T. Bojić, S.U. Pavlović, N.N. Radovanović, A. Kalauzi, Uncoupling of cardiac and respiratory rhythm in atrial fibrillation. Biomed. Tech. (Berlin) 61, 657–663 (2016)CrossRefGoogle Scholar
  47. 47.
    G.B. Moody, W.K. Muldrow, R.G. Mark, A noise stress test for arrhythmia detectors. Proc. Comput. Cardiol. 11, 381–384 (1984)Google Scholar
  48. 48.
    M.S. Guillem, A.V. Sahakian, S. Swiryn, Derivation of orthogonal leads from the 12-lead electrocardiogram. Performance of an atrial-based transform for the derivation of P loops. J. Electrocardiol. 41, 19–25 (2008)CrossRefGoogle Scholar
  49. 49.
    G.E. Dower, A lead synthesizer for the Frank system to simulate the standard 12-lead electrocardiogram. J. Electrocardiol. 1, 101–116 (1968)CrossRefGoogle Scholar
  50. 50.
    G.E. Dower, H.B. Machado, J.A. Osborne, On deriving the electrocardiogram from vectorcardiographic leads. Clin. Cardiol. 3, 87–95 (1980)Google Scholar
  51. 51.
    E.T.Y. Chang, Y.T. Lin, T. Galla, R.H. Clayton, J. Eatock, A stochastic individual-based model of the progression of atrial fibrillation in individuals and populations. PLoS ONE 11, e0152349 (2016)CrossRefGoogle Scholar
  52. 52.
    M.C. Wijffels, C.J. Kirchhof, R. Dorland, M.A. Allessie, Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats. Circulation 92, 1954–1968 (1995)CrossRefGoogle Scholar
  53. 53.
    C.R. Kerr, K.H. Humphries, M. Talajic, G.J. Klein, S.J. Connolly, M. Green, J. Boone, R. Sheldon, P. Dorian, D. Newman, Progression to chronic atrial fibrillation after the initial diagnosis of paroxysmal atrial fibrillation: results from the Canadian Registry of Atrial Fibrillation. Am. Heart J. 149, 489–496 (2005)CrossRefGoogle Scholar
  54. 54.
    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)CrossRefGoogle Scholar
  55. 55.
    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)CrossRefGoogle Scholar
  56. 56.
    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)CrossRefGoogle Scholar
  57. 57.
    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)CrossRefGoogle Scholar
  58. 58.
    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)CrossRefGoogle Scholar
  59. 59.
    A. van Oosterom, T.F. Oostendorp, ECGSIM: an interactive tool for studying the genesis of QRST waveforms. Heart 90, 165–168 (2004)CrossRefGoogle Scholar
  60. 60.
    J. Behar, F. Andreotti, S. Zaunseder, Q. Li, J. Oster, G.D. Clifford, An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings. Physiol. Meas. 35, 1537–1550 (2014)CrossRefGoogle 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 Institute, Kaunas University of TechnologyKaunasLithuania

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