Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
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)
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)
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)
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)
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)
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)
M. Stridh, L. Sörnmo, Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation. IEEE Trans. Biomed. Eng. 48, 105–111 (2001)
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)
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)
R. Alcaraz, J.J. Rieta, Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination. Comput. Biol. Med. 39, 697–706 (2009)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
J. Malik, N. Reed, C.-L. Wang, H.-T. Wu, Single-lead f-wave extraction using diffusion geometry. Physiol. Meas. 38, 1310–1334 (2017)
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)
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)
G.R. Pai, J.M. Rawles, The QT interval in atrial fibrillation. Brit. Heart J. 61, 510–513 (1989)
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)
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)
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)
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)
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)
A. Sandryhaila, S. Saba, M. Puschel, J. Kovacevic, Efficient compression of QRS complexes using Hermite expansion. IEEE Trans. Signal Process. 60, 947–955 (2012)
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)
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)
H.C. Bazett, An analysis of the time relations of electrocardiograms. Heart 7, 353–370 (1920)
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)
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)
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)
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)
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)
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)
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)
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)
G.B. Moody, W.K. Muldrow, R.G. Mark, A noise stress test for arrhythmia detectors. Proc. Comput. Cardiol. 11, 381–384 (1984)
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)
G.E. Dower, A lead synthesizer for the Frank system to simulate the standard 12-lead electrocardiogram. J. Electrocardiol. 1, 101–116 (1968)
G.E. Dower, H.B. Machado, J.A. Osborne, On deriving the electrocardiogram from vectorcardiographic leads. Clin. Cardiol. 3, 87–95 (1980)
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)
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)
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)
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)
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)
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)
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)
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)
A. van Oosterom, T.F. Oostendorp, ECGSIM: an interactive tool for studying the genesis of QRST waveforms. Heart 90, 165–168 (2004)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Sörnmo, L., Petrėnas, A., Marozas, V. (2018). Databases and Simulation . In: Sörnmo, L. (eds) Atrial Fibrillation from an Engineering Perspective. Series in BioEngineering. Springer, Cham. https://doi.org/10.1007/978-3-319-68515-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-68515-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68513-7
Online ISBN: 978-3-319-68515-1
eBook Packages: EngineeringEngineering (R0)