Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes

OBJECTIVE A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed. SIGNIFICANCE The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking. APPROACH The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise. MAIN RESULTS Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties.

[1]  R. Virmani,et al.  Embolic Myocardial Infarction as a Consequence of Atrial Fibrillation: A Prevailing Disease of the Future. , 2015, Circulation.

[2]  Witold Pedrycz,et al.  ECG signal processing, classification, and interpretation : , 2012 .

[3]  J. Millet,et al.  Noninvasive Localization of Maximal Frequency Sites of Atrial Fibrillation by Body Surface Potential Mapping , 2013, Circulation. Arrhythmia and electrophysiology.

[4]  S. Mittal,et al.  Correlation of QT interval correction methods during atrial fibrillation and sinus rhythm. , 2013, The American journal of cardiology.

[5]  Julien Oster,et al.  An artificial model of the electrocardiogram during paroxysmal atrial fibrillation , 2013, Computing in Cardiology 2013.

[6]  H. Bazett,et al.  AN ANALYSIS OF THE TIME‐RELATIONS OF ELECTROCARDIOGRAMS. , 1997 .

[7]  Valentina D. A. Corino,et al.  Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation , 2013, Biomed. Signal Process. Control..

[8]  Leif Sörnmo,et al.  Spatiotemporal QRST cancellation techniques for analysis of atrial fibrillation , 2001, IEEE Transactions on Biomedical Engineering.

[9]  P. Laguna,et al.  Adaptive estimation of QRS complex wave features of ECG signal by the hermite model , 2007, Medical and Biological Engineering and Computing.

[10]  Ying Sun,et al.  Gaussian pulse decomposition: An intuitive model of electrocardiogram waveforms , 1997, Annals of Biomedical Engineering.

[11]  K. Kelly,et al.  Atrial fibrillation detected by mobile cardiac outpatient telemetry in cryptogenic TIA or stroke , 2008, Neurology.

[12]  S. Connolly,et al.  Progression to chronic atrial fibrillation after the initial diagnosis of paroxysmal atrial fibrillation: results from the Canadian Registry of Atrial Fibrillation. , 2005, American Heart Journal.

[13]  Chao Huang,et al.  A Novel Method for Detection of the Transition Between Atrial Fibrillation and Sinus Rhythm , 2011, IEEE Transactions on Biomedical Engineering.

[14]  Ye Li,et al.  Atrial activity extraction from single lead ECG recordings: Evaluation of two novel methods , 2013, Comput. Biol. Medicine.

[15]  Sebastian Zaunseder,et al.  An ECG simulator for generating maternal-foetal activity mixtures on abdominal ECG recordings , 2014, Physiological measurement.

[16]  A. Go,et al.  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 , 2012, Stroke.

[17]  Ki H. Chon,et al.  Time-Varying Coherence Function for Atrial Fibrillation Detection , 2013, IEEE Transactions on Biomedical Engineering.

[18]  Valentina D. A. Corino,et al.  An Atrioventricular Node Model for Analysis of the Ventricular Response During Atrial Fibrillation , 2011, IEEE Transactions on Biomedical Engineering.

[19]  Olle Pahlm,et al.  A Method for Evaluation of QRS Shape Features Using a Mathematical Model for the ECG , 1981, IEEE Transactions on Biomedical Engineering.

[20]  Steven M Bradley,et al.  Early detection of occult atrial fibrillation and stroke prevention , 2015, Heart.

[21]  Patrick E. McSharry,et al.  A dynamical model for generating synthetic electrocardiogram signals , 2003, IEEE Transactions on Biomedical Engineering.

[22]  Kennedy R. Lees,et al.  Detection of Atrial Fibrillation After Ischemic Stroke or Transient Ischemic Attack: A Systematic Review and Meta-Analysis , 2014, Stroke.

[23]  MARCELLO DISERTORI,et al.  Deterioration of Organization in the First Minutes of Atrial Fibrillation: A Beat‐to‐Beat Analysis of Cycle Length and Wave Similarity , 2007, Journal of cardiovascular electrophysiology.

[24]  Raymond C S Seet,et al.  Paroxysmal atrial fibrillation in cryptogenic stroke: a case-control study. , 2013, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[25]  Michael T. Mullen,et al.  Predictors of Finding Occult Atrial Fibrillation After Cryptogenic Stroke , 2015, Stroke.

[26]  Steven Swiryn,et al.  Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans. , 2007, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[27]  Raúl Alcaraz,et al.  Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation , 2009, Comput. Methods Programs Biomed..

[28]  Vaidotas Marozas,et al.  An Echo State Neural Network for QRST Cancellation During Atrial Fibrillation , 2012, IEEE Transactions on Biomedical Engineering.

[29]  A. Mahajan,et al.  The Evolution and Application of Cardiac Monitoring for Occult Atrial Fibrillation in Cryptogenic Stroke and TIA , 2016, Current Treatment Options in Neurology.

[30]  P Caminal,et al.  Automatic detection of wave boundaries in multilead ECG signals: validation with the CSE database. , 1994, Computers and biomedical research, an international journal.

[31]  Vijayshree Yadav,et al.  Use of Cannabinoids for Spasticity and Pain Management in MS , 2015, Current Treatment Options in Neurology.

[32]  G. Lip,et al.  Stroke prevention in atrial fibrillation , 2016, The Lancet.

[33]  Jelena Kovacevic,et al.  Efficient Compression of QRS Complexes Using Hermite Expansion , 2012, IEEE Transactions on Signal Processing.

[34]  Giovanni Calcagnini,et al.  P-Wave Morphology Assessment by a Gaussian Functions-Based Model in Atrial Fibrillation Patients , 2007, IEEE Transactions on Biomedical Engineering.

[35]  Emma Pickwell-MacPherson,et al.  Automatic online detection of atrial fibrillation based on symbolic dynamics and Shannon entropy , 2014, BioMedical Engineering OnLine.

[36]  Roberto Sassi,et al.  Analysis of Surface Atrial Signals: Time Series with Missing Data? , 2009, Annals of Biomedical Engineering.

[37]  Jan Gierałtowski,et al.  Modeling heart rate variability including the effect of sleep stages. , 2016, Chaos.

[38]  Mattias Ohlsson,et al.  Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks , 2004, Artif. Intell. Medicine.

[39]  M. Harris,et al.  Model-Based Atrial Fibrillation Detection , 2012 .

[40]  Pablo Laguna,et al.  Variability of Ventricular Repolarization Dispersion Quantified by Time-Warping the Morphology of the T-Waves , 2017, IEEE Transactions on Biomedical Engineering.

[41]  Panos Vardas,et al.  Prognosis and treatment of atrial fibrillation patients by European cardiologists: one year follow-up of the EURObservational Research Programme-Atrial Fibrillation General Registry Pilot Phase (EORP-AF Pilot registry). , 2014, European heart journal.

[42]  G. Dower,et al.  On Deriving the Electrocardiogram from Vectorcardiographic Leads , 1980, Clinical cardiology.

[43]  Ralf Bousseljot,et al.  Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet , 2009 .

[44]  Vaidotas Marozas,et al.  A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation , 2013, Computing in Cardiology 2013.

[45]  Vaidotas Marozas,et al.  Detection of occult paroxysmal atrial fibrillation , 2014, Medical & Biological Engineering & Computing.

[46]  Behnaz Ghoraani,et al.  Rate-independent detection of atrial fibrillation by statistical modeling of atrial activity , 2015, Biomed. Signal Process. Control..

[47]  Steve Enger,et al.  Abnormal atrial activation in young patients with lone atrial fibrillation. , 2011, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[48]  Steven Swiryn,et al.  Derivation of orthogonal leads from the 12-lead electrocardiogram. Performance of an atrial-based transform for the derivation of P loops. , 2008, Journal of electrocardiology.

[49]  Travis J. Moss,et al.  Heart rate dynamics distinguish among atrial fibrillation, normal sinus rhythm and sinus rhythm with frequent ectopy , 2015, Physiological measurement.

[50]  S. Connolly,et al.  Progression to chronic atrial fibrillation after the initial diagnosis of paroxysmal atrial fibrillation: results from the Canadian Registry of Atrial Fibrillation. , 2005, American heart journal.

[51]  Raúl Alcaraz,et al.  Surface ECG organization analysis to predict paroxysmal atrial fibrillation termination , 2009, Comput. Biol. Medicine.

[52]  J M Rawles,et al.  The QT interval in atrial fibrillation. , 1989, British heart journal.

[53]  David J. Gladstone,et al.  Atrial Premature Beats Predict Atrial Fibrillation in Cryptogenic Stroke: Results From the EMBRACE Trial , 2015, Stroke.

[54]  Christian Jutten,et al.  Multichannel ECG and Noise Modeling: Application to Maternal and Fetal ECG Signals , 2007, EURASIP J. Adv. Signal Process..

[55]  A. Rabinstein,et al.  Atrial fibrillation detected by mobile cardiac outpatient telemetry in cryptogenic TIA or stroke , 2009 .

[56]  Sheng Lu,et al.  Automatic Real Time Detection of Atrial Fibrillation , 2009, Annals of Biomedical Engineering.

[57]  James McNames,et al.  Prediction of paroxysmal atrial fibrillation by analysis of atrial premature complexes , 2004, IEEE Transactions on Biomedical Engineering.

[58]  P. Andersen,et al.  New‐Onset Atrial Fibrillation is Associated With Cardiovascular Events Leading to Death in a First Time Myocardial Infarction Population of 89 703 Patients With Long‐Term Follow‐Up: A Nationwide Study , 2014, Journal of the American Heart Association.

[59]  Vaidotas Marozas,et al.  Low-complexity detection of atrial fibrillation in continuous long-term monitoring , 2015, Comput. Biol. Medicine.

[60]  Leif Sörnmo,et al.  Sequential characterization of atrial tachyarrhythmias based on ECG time-frequency analysis , 2004, IEEE Transactions on Biomedical Engineering.

[61]  M. Masè,et al.  Dynamics of AV coupling during human atrial fibrillation: role of atrial rate. , 2015, American journal of physiology. Heart and circulatory physiology.

[62]  Faisal Rahman,et al.  Global epidemiology of atrial fibrillation , 2014, Nature Reviews Cardiology.

[63]  Alberto Herreros,et al.  Age-related changes in P wave morphology in healthy subjects , 2007, BMC cardiovascular disorders.

[64]  M. Allessie,et al.  Atrial fibrillation begets atrial fibrillation. A study in awake chronically instrumented goats. , 1995, Circulation.

[65]  E. Helfenbein,et al.  Improvements in atrial fibrillation detection for real-time monitoring. , 2009, Journal of electrocardiology.

[66]  S. Osowski,et al.  On-line heart beat recognition using hermite polynomials and neuro-fuzzy network , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[67]  Leif Sörnmo,et al.  Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties , 2001, IEEE Transactions on Biomedical Engineering.

[68]  P. Platonov,et al.  Electrocardiographic and Echocardiographic predictors of paroxysmal atrial fibrillation detected after ischemic stroke , 2016, BMC Cardiovascular Disorders.