The Contribution of Nonlinear Methods in the Understanding of Atrial Fibrillation

Analysis of cardiac time series by nonlinear metrics has recently gained great interest, because the latter observations suggest that the mechanisms involved in cardiovascular regulation likely interact with each other in a nonlinear way [1]. Furthermore, chaotic behavior can be appreciated in the diseased heart with atrial fibrillation (AF) at cellular level and atrial electrophysiological remodeling during this arrhythmia is a far-from-linear process [2]. Hence, the purpose of this chapter is to review the use of nonlinear methods in the analysis of AF, highlighting the clinically useful revealed information that can improve the understanding of this arrhythmia mechanisms and the existing treatments.

[1]  Francisco Castells,et al.  Noninvasive Mapping of Human Atrial Fibrillation , 2009, Cardiovascular Electrophysiology.

[2]  R B Schuessler,et al.  RR interval dynamics before atrial fibrillation in patients after coronary artery bypass graft surgery. , 1998, Circulation.

[3]  R. Sassi,et al.  On predicting the spontaneous termination of atrial fibrillation episodes using linear and nonlinear parameters of ECG signal and RR series , 2004, Computers in Cardiology, 2004.

[4]  Giovanni Calcagnini,et al.  Endocardial Mapping of Atrial Fibrillation with Basket Catheter , 2006 .

[5]  Dong-Gu Shin,et al.  Prediction of paroxysmal atrial fibrillation using nonlinear analysis of the R-R interval dynamics before the spontaneous onset of atrial fibrillation. , 2006, Circulation journal : official journal of the Japanese Circulation Society.

[6]  S. Sakata,et al.  Circadian rhythms of atrioventricular conduction properties in chronic atrial fibrillation with and without heart failure. , 1998, Journal of the American College of Cardiology.

[7]  S. Cerutti,et al.  Linear and nonlinear coupling between atrial signals , 2006, IEEE Engineering in Medicine and Biology Magazine.

[8]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[9]  A. Berkowitsch,et al.  Electrophysiological Heterogeneity of Atrial Fibrillation and Local Effect of Propafenone in the Human Right Atrium: Analysis Based on Symbolic Dynamics , 2000, Journal of Interventional Cardiac Electrophysiology.

[10]  G. Calcagnini,et al.  Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes , 2006, Medical and Biological Engineering and Computing.

[11]  Luca Faes,et al.  Quantification of synchronization during atrial fibrillation by Shannon entropy: validation in patients and computer model of atrial arrhythmias , 2005, Physiological measurement.

[12]  Atul Verma,et al.  A prospective, multicenter evaluation of ablating complex fractionated electrograms (CFEs) during atrial fibrillation (AF) identified by an automated mapping algorithm: acute effects on AF and efficacy as an adjuvant strategy. , 2008, Heart rhythm.

[13]  A. Bolz,et al.  Algorithm for Identifying Patients with Paroxysmal Atrial Fibrillation without Appearance on the ECG , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[15]  Raúl Alcaraz,et al.  Short-time regularity assessment of fibrillatory waves from the surface ECG in atrial fibrillation , 2012, Physiological measurement.

[16]  N. Kouchoukos,et al.  Characterization of Atrial Fibrillation in Man: Studies Following Open Heart Surgery * , 1978, Pacing and clinical electrophysiology : PACE.

[17]  Luca T Mainardi,et al.  On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[18]  Niels Wessel,et al.  Addressing the complexity of cardiovascular regulation , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  Yuriy V. Chesnokov,et al.  Complexity and spectral analysis of the heart rate variability dynamics for distant prediction of paroxysmal atrial fibrillation with artificial intelligence methods , 2008, Artif. Intell. Medicine.

[20]  J Clémenty,et al.  Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. , 1998, The New England journal of medicine.

[21]  Raúl Alcaraz,et al.  Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation. , 2011, Medical engineering & physics.

[22]  L. Mainardi,et al.  Assessment of spatial organization in the atria during paroxysmal atrial fibrillation and adrenergic stimulation , 2006, Biomedizinische Technik. Biomedical engineering.

[23]  S. Shkurovich,et al.  Detection of atrial activity from high-voltage leads of implantable ventricular defibrillators using a cancellation technique , 1998, IEEE Transactions on Biomedical Engineering.

[24]  George Sugihara,et al.  Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series , 1990, Nature.

[25]  R. Lux,et al.  Heart Rate Variability Measures During Sinus Rhythm Predict Cycle Length Entropy During Atrial Fibrillation , 2008, Journal of cardiovascular electrophysiology.

[26]  M. Jeon,et al.  Atrial fibrillation detection by heart rate variability in Poincare plot , 2009, Biomedical engineering online.

[27]  W. Stevenson,et al.  Recording Techniques for Clinical Electrophysiology , 2005, Journal of cardiovascular electrophysiology.

[28]  OmerBerenfeld,et al.  Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans , 2005 .

[29]  Raúl Alcaraz,et al.  The application of nonlinear metrics to assess organization differences in short recordings of paroxysmal and persistent atrial fibrillation , 2010, Physiological measurement.

[30]  R. Sun,et al.  Predicting spontaneous termination of atrial fibrillation based on the RR interval. , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[31]  A. Berkowitsch,et al.  Algorithmic complexity. A new approach of non-linear algorithms for the analysis of atrial signals from multipolar basket catheter. , 2001, Annali dell'Istituto superiore di sanita.

[32]  J. R. Moorman,et al.  Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices. , 2011, American journal of physiology. Heart and circulatory physiology.

[33]  Jaap Haaksma,et al.  Clustering of RR Intervals Predicts Effective Electrical Cardioversion for Atrial Fibrillation , 2004, Journal of cardiovascular electrophysiology.

[34]  N. Ohte,et al.  Reduced ventricular response irregularity is associated with increased mortality in patients with chronic atrial fibrillation. , 2000, Circulation.

[35]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[36]  F. Gaita,et al.  Different patterns of atrial activation in idiopathic atrial fibrillation: simultaneous multisite atrial mapping in patients with paroxysmal and chronic atrial fibrillation. , 2001, Journal of the American College of Cardiology.

[37]  Donald M Bers,et al.  Human atrial fibrillation: insights from computational electrophysiological models. , 2011, Trends in cardiovascular medicine.

[38]  J. J. Rieta,et al.  Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms , 2008, Physiological measurement.

[39]  Fredrik Holmqvist,et al.  Atrial fibrillatory rate and irregularity of ventricular response as predictors of clinical outcome in patients with atrial fibrillation. , 2011, Journal of electrocardiology.

[40]  Sung Rr,et al.  Predicting spontaneous termination of atrial fibrillation based on the RR interval , 2009 .

[41]  Volkan Tuzcu,et al.  Decrease in the heart rate complexity prior to the onset of atrial fibrillation. , 2006, 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.

[42]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization. A time domain perspective in the surface ECG. , 2006, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[43]  N. Ohte,et al.  Circadian rhythm of atrioventricular conduction predicts long-term survival in patients with chronic atrial fibrillation , 2002 .

[44]  Sergio Cerutti,et al.  Assessment of the dynamics of atrial signals and local atrial period series during atrial fibrillation: effects of isoproterenol administration , 2004, Biomedical engineering online.

[45]  Sergio Cerutti,et al.  Linear and nonlinear coupling between atrial signals. Three methods for the analysis of the relationships among atrial electrical activities in different sites. , 2006, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[46]  José Millet-Roig,et al.  PoincarÉ Surface Profiles of RR Intervals: A Novel Noninvasive Method for the Evaluation of Preferential AV Nodal Conduction During Atrial Fibrillation , 2009, IEEE Transactions on Biomedical Engineering.

[47]  Maurits A. Allessie,et al.  Nonlinear analysis of the pharmacological conversion of sustained atrial fibrillation in conscious goats by the class Ic drug cibenzoline. , 1997, Chaos.

[48]  J. Horrow,et al.  Heart rate variability measures poorly predict atrial fibrillation after off-pump coronary artery bypass grafting. , 2011, Journal of clinical anesthesia.

[49]  J. J. Rieta,et al.  Comparative study of methods for ventricular activity cancellation in atrial electrograms of atrial fibrillation , 2007, Physiological measurement.

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

[51]  Raúl Alcaraz,et al.  Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion , 2009, Medical & Biological Engineering & Computing.

[52]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[53]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[54]  Valentina Fiore,et al.  Linear and Nonlinear Heart Rate Variability Indexes in Clinical Practice , 2012, Comput. Math. Methods Medicine.

[55]  Jason Ng,et al.  Measuring the Complexity of Atrial Fibrillation Electrograms , 2010, Journal of cardiovascular electrophysiology.

[56]  H. Kantz,et al.  Nonlinear time series analysis , 1997 .

[57]  M. Allessie,et al.  Experimental evaluation of Moe's multiple wavelet hypothesis of atrial fibrillation , 1985 .

[58]  H. Pitschner,et al.  Multielectrode basket catheter mapping for human atrial fibrillation. , 1998, Journal of cardiovascular electrophysiology.

[59]  Nonlinear Heart Rate Variability Analysis May Predict Atrial Fibrillation After Coronary Artery Bypass Grafting , 2006, Anesthesia and analgesia.

[60]  H. Ohnishi,et al.  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 , 1998 .

[61]  J. J. Rieta,et al.  Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings , 2012, Biomedical engineering online.

[62]  V. Barbaro,et al.  Automated Classification of Human Atrial Fibrillation From Intraatrial Electrograms , 2000, Pacing and clinical electrophysiology : PACE.

[63]  Raúl Alcaraz,et al.  Assessment of non-invasive time and frequency atrial fibrillation organization markers with unipolar atrial electrograms , 2011, Physiological measurement.

[64]  R. Thuraisingham.  An electrocardiogram marker to detect paroxysmal atrial fibrillation. , 2007, Journal of electrocardiology.

[65]  G. Calcagnini,et al.  Descriptors of wavefront propagation , 2006, IEEE Engineering in Medicine and Biology Magazine.

[66]  Haitham M. Al-Angari,et al.  Atrial fibrillation and waveform characterization , 2006, IEEE Engineering in Medicine and Biology Magazine.

[67]  Raúl Alcaraz,et al.  A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms , 2010, Biomed. Signal Process. Control..

[68]  J. Jalife,et al.  Temporal Organization of Atrial Activity and Irregular Ventricular Rhythm During Spontaneous Atrial Fibrillation: , 2000, Journal of cardiovascular electrophysiology.

[69]  Pablo Laguna,et al.  Bioelectrical Signal Processing in Cardiac and Neurological Applications , 2005 .

[70]  Hassan Ghassemian,et al.  Prediction of paroxysmal atrial fibrillation based on non-linear analysis and spectrum and bispectrum features of the heart rate variability signal , 2012, Comput. Methods Programs Biomed..

[71]  Yuanyuan Wang,et al.  Predicting termination of atrial fibrillation based on the structure and quantification of the recurrence plot. , 2008, Medical engineering & physics.

[72]  Chih-Cheng Lu,et al.  Differentiation of Atrial Flutter and Atrial Fibrillation from Surface Electrocardiogram Using Nonlinear Analysis , 2005 .

[73]  Jeff Walden,et al.  Ventricular response in atrial fibrillation: random or deterministic? , 1999, American journal of physiology. Heart and circulatory physiology.

[74]  José Millet-Roig,et al.  Atrial activity extraction for atrial fibrillation analysis using blind source separation , 2004, IEEE Transactions on Biomedical Engineering.

[75]  Luca Faes,et al.  A method for quantifying atrial fibrillation organization based on wave-morphology similarity , 2002, IEEE Transactions on Biomedical Engineering.

[76]  H. Ghassemian,et al.  Prediction of paroxysmal atrial fibrillation using recurrence plot-based features of the RR-interval signal , 2011, Physiological measurement.

[77]  K. Aminian,et al.  Nonlinear analysis of human physical activity patterns in health and disease. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[78]  R. Cohen,et al.  Cardiac Arrhythmias Imprint Specific Signatures on Lorenz Plots , 2008, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[79]  Youhua Zhang,et al.  Ventricular Rate Control During Atrial Fibrillation and AV Node Modifications: , 2004, Pacing and clinical electrophysiology : PACE.

[80]  Giovanni Calcagnini,et al.  Non-linear coupling of atrial activation processes during atrial fibrillation in humans , 2001, Biological Cybernetics.

[81]  César Sánchez,et al.  Atrial fibrillation organization: quantification of propofol effects , 2009, Medical & Biological Engineering & Computing.

[82]  S. Cerutti,et al.  Recurrent Patterns of Atrial Depolarization During Atrial Fibrillation Assessed by Recurrence Plot Quantification , 2004, Annals of Biomedical Engineering.

[83]  M. Allessie,et al.  Nonlinear Analysis of Epicardial Atrial Electrograms of Electrically Induced Atrial Fibrillation in Man , 1995, Journal of cardiovascular electrophysiology.

[84]  M. Allessie,et al.  High-density mapping of electrically induced atrial fibrillation in humans. , 1994, Circulation.

[85]  Jean-Marc Vesin,et al.  Cancellation of Ventricular Activity in the ECG: Evaluation of Novel and Existing Methods , 2007, IEEE Transactions on Biomedical Engineering.

[86]  H. Huikuri,et al.  Altered complexity and correlation properties of R-R interval dynamics before the spontaneous onset of paroxysmal atrial fibrillation. , 1999, Circulation.

[87]  M. Allessie,et al.  Configuration of unipolar atrial electrograms during electrically induced atrial fibrillation in humans. , 1997, Circulation.

[88]  V. Booth,et al.  Causal entropies—A measure for determining changes in the temporal organization of neural systems , 2007, Journal of Neuroscience Methods.

[89]  Leif Sörnmo,et al.  Validation and Clinical Application of Time‐Frequency Analysis of Atrial Fibrillation Electrocardiograms , 2007, Journal of cardiovascular electrophysiology.

[90]  S Cerutti,et al.  Discrimination of atrial rhythms by linear and non-linear methods. , 2001, Annali dell'Istituto superiore di sanita.

[91]  J. Millet,et al.  Entropy measurements in paroxysmal and persistent atrial fibrillation , 2010, Physiological measurement.