Extraction of f Waves

This chapter provides a comprehensive overview of methods for f wave extraction, divided into the following categories: average beat subtraction and variants, interpolation, extended Kalman filtering, adaptive filtering, principal component analysis, singular spectral analysis, autoregressive modeling and prediction error analysis, and independent component analysis. Different performance measures are described, used either for real or simulated ECG signals.

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

[2]  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.

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

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

[5]  Andreas Bollmann,et al.  Waveform Characterization of Atrial Fibrillation Using Phase Information , 2009, IEEE Transactions on Biomedical Engineering.

[6]  N.V. Thakor,et al.  Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection , 1991, IEEE Transactions on Biomedical Engineering.

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

[8]  Fernando S. Schlindwein,et al.  QRS subtraction for atrial electrograms: flat, linear and spline interpolations , 2011, Medical & Biological Engineering & Computing.

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

[10]  Michel Haissaguerre,et al.  QRS Subtraction and the ECG Analysis of Atrial Ectopics , 2004, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[11]  Roberto Sassi,et al.  Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals' power. , 2013, Medical engineering & physics.

[12]  Esteban J. Pino,et al.  Atrial activity selection for atrial fibrillation ECG recordings , 2013, Comput. Biol. Medicine.

[13]  ADRIAAN van OOSTEROM.  The Dominant T Wave and Its Significance , 2003 .

[14]  V. Jacquemet,et al.  Analysis of electrocardiograms during atrial fibrillation , 2006, IEEE Engineering in Medicine and Biology Magazine.

[15]  L Sörnmo,et al.  Vectorcardiographic loop alignment and morphologic beat-to-beat variability. , 1998, IEEE transactions on bio-medical engineering.

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

[17]  Andrew J. Pullan,et al.  Mathematically Modelling the Electrical Activity of the Heart: From Cell to Body Surface and Back Again , 2005 .

[18]  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.

[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]  Roberto Sassi,et al.  An Estimate of the Dispersion of Repolarization Times Based on a Biophysical Model of the ECG , 2011, IEEE Transactions on Biomedical Engineering.

[21]  Q. Henry Wu,et al.  MCPSO: A multi-swarm cooperative particle swarm optimizer , 2007, Appl. Math. Comput..

[22]  Herbert Jaeger,et al.  Optimization and applications of echo state networks with leaky- integrator neurons , 2007, Neural Networks.

[23]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[24]  P. Caminal,et al.  Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques , 1992, IEEE Transactions on Biomedical Engineering.

[25]  L. Sörnmo,et al.  Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method. , 1998, Cardiovascular research.

[26]  Emanuel Parzen,et al.  ON SPECTRAL ANALYSIS WITH MISSING OBSERVATIONS AND AMPLITUDE MODULATION , 1962 .

[27]  James A. Anderson,et al.  An Introduction To Neural Networks , 1998 .

[28]  L Stark,et al.  REMOTE REAL‐TIME DIAGNOSIS OF CLINICAL ELECTROCARDIOGRAMS BY A DIGITAL COMPUTER SYSTEM * , 1965, Annals of the New York Academy of Sciences.

[29]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[30]  Thomas Leuthold,et al.  Analysis of atrial fibrillatory activity from high-resolution surface electrocardiograms: Evaluation and application of a new system. , 2008, Experimental and clinical cardiology.

[31]  Jean-Marc Vesin,et al.  Suppression of Ventricular Activity in the Surface Electrocardiogram of Atrial Fibrillation , 2004, ICA.

[32]  Martin Stridh,et al.  Time-frequency analysis of the surface electrocardiogram for monitoring antiarrhythmic drug effects in atrial fibrillation. , 2005, The American journal of cardiology.

[33]  V. Zarzoso,et al.  Extraction of the Atrial Activity from the ECG based on Independent Component Analysis with Prior Knowledge of the Source Kurtosis Signs , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Olivier Meste,et al.  Weighted averaging using adaptive estimation of the weights , 1995, Signal Process..

[35]  Barak A. Pearlmutter.  Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[36]  Christian Jutten,et al.  A Nonlinear Bayesian Filtering Framework for ECG Denoising , 2007, IEEE Transactions on Biomedical Engineering.

[37]  Saeed V. Vaseghi,et al.  Advanced Digital Signal Processing and Noise Reduction: Vaseghi/Advanced Digital Signal Processing and Noise Reduction, 3rd Edition , 2006 .

[38]  David Moratal,et al.  Estimation of atrial fibrillatory wave from single-lead atrial fibrillation electrocardiograms using principal component analysis concepts , 2005, Medical and Biological Engineering and Computing.

[39]  Raúl Alcaraz,et al.  Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms , 2015, Entropy.

[40]  Leif Sörnmo,et al.  Decrease of the atrial fibrillatory rate, increased organization of the atrial rhythm and termination of atrial fibrillation by AZD7009. , 2013, Journal of electrocardiology.

[41]  D A BRODY,et al.  Principal Factor Waveforms of the Thoracic QRS Complex , 1964, Circulation research.

[42]  Philip Langley,et al.  Wavelet Entropy as a Measure of Ventricular Beat Suppression from the Electrocardiogram in Atrial Fibrillation , 2015, Entropy.

[43]  O Meste,et al.  Ventricular activity residual reduction in remainder ECGs based on short-term autoregressive model interpolation , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[44]  Pierre Comon,et al.  Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques , 2013 .

[45]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[46]  G. S. Wagner.  Comprar Marriott's Practical Electrocardiography | Wagner, G.S. | 9781451146257 | Lippincott Williams & Wilkins , 2013 .

[47]  Akira Fujiki,et al.  Role of fibrillation cycle length in spontaneous and drug-induced termination of human atrial fibrillation. , 2003, Circulation journal : official journal of the Japanese Circulation Society.

[48]  Mark Sterling,et al.  Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation , 2015, Comput. Math. Methods Medicine.

[49]  Gang Wang,et al.  f‐Wave Suppression Method for Improvement of Locating T‐Wave Ends in Electrocardiograms during Atrial Fibrillation , 2013, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[50]  Jorge Igual,et al.  A fixed point algorithm for extracting the atrial activity in the frequency domain , 2010, Comput. Biol. Medicine.

[51]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[52]  S Swiryn,et al.  Computer detection of atrioventricular dissociation from surface electrocardiograms during wide QRS complex tachycardias. , 1985, Circulation.

[53]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[54]  Jorge Igual,et al.  Exploiting periodicity to extract the atrial activity in atrial arrhythmias , 2011, EURASIP J. Adv. Signal Process..

[55]  A. van Oosterom,et al.  Heart position and orientation in forward and inverse electrocardiography , 1992, Medical and Biological Engineering and Computing.

[56]  José Millet-Roig,et al.  Noninvasive Assessment of the Complexity and Stationarity of the Atrial Wavefront Patterns During Atrial Fibrillation , 2010, IEEE Transactions on Biomedical Engineering.

[57]  P. Langley,et al.  Frequency analysis of atrial fibrillation , 2000, Computers in Cardiology 2000. Vol.27 (Cat. 00CH37163).

[58]  Vicente Zarzoso,et al.  Extraction of ECG Characteristics Using Source Separation Techniques: Exploiting Statistical Independence and Beyond , 2009 .

[59]  L. Sörnmo,et al.  Sampling rate and the estimation of ensemble variability for repetitive signals , 2006, Medical and Biological Engineering and Computing.

[60]  I. Jolliffe.  Principal Component Analysis , 2002 .

[61]  José Millet-Roig,et al.  Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias , 2005, IEEE Transactions on Biomedical Engineering.

[62]  José Carlos Príncipe,et al.  Analysis and Design of Echo State Networks , 2007, Neural Computation.

[63]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[64]  Gang Wang,et al.  Extraction of Desired Signal Based on AR Model with Its Application to Atrial Activity Estimation in Atrial Fibrillation , 2008, EURASIP J. Adv. Signal Process..

[65]  Ning Xiang,et al.  Bayesian Signal Processing: Classical, Modern, and Particle Filtering Methods, 2nd Ed. , 2017, The Journal of the Acoustical Society of America.

[66]  Guy Carrault,et al.  Atrial activity enhancement by Wiener filtering using an artificial neural network , 2001, IEEE Transactions on Biomedical Engineering.

[67]  Roberto Sassi,et al.  An Extended Bayesian Framework for Atrial and Ventricular Activity Separation in Atrial Fibrillation , 2017, IEEE Journal of Biomedical and Health Informatics.

[68]  R. Willems,et al.  Determination of atrial fibrillation frequency using QRST-cancellation with QRS-scaling in standard electrocardiogram leads , 2005, Computers in Cardiology, 2005.

[69]  C. Li,et al.  Detection of ECG characteristic points using wavelet transforms. , 1995, IEEE transactions on bio-medical engineering.

[70]  Michael Ghil,et al.  Spatio-temporal filling of missing points in geophysical data sets , 2006 .

[71]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[72]  Jean-Marc Vesin,et al.  Adaptive frequency tracking of the baseline ECG identifies the site of atrial fibrillation termination by catheter ablation , 2013, Biomed. Signal Process. Control..

[73]  Pablo Laguna,et al.  Detection of body position changes from the ECG using a Laplacian noise model , 2014, Biomed. Signal Process. Control..

[74]  Raúl Alcaraz,et al.  Ventricular activity morphological characterization: Ectopic beats removal in long term atrial fibrillation recordings , 2013, Comput. Methods Programs Biomed..

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

[76]  Pablo Laguna,et al.  A robust method for ECG-based estimation of the respiratory frequency during stress testing , 2006, IEEE Transactions on Biomedical Engineering.

[77]  A. Sahakian,et al.  The effect of QRS cancellation on atrial fibrillatory wave signal characteristics in the surface electrocardiogram. , 2003, Journal of electrocardiology.

[78]  Pablo Laguna,et al.  Multilead ECG Delineation Using Spatially Projected Leads From Wavelet Transform Loops , 2009, IEEE Transactions on Biomedical Engineering.

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

[80]  Pablo Laguna,et al.  The adaptive linear combiner with a periodic-impulse reference input as a linear comb filter , 1996, Signal Process..

[81]  Pablo Laguna,et al.  Vectorcardiographic loop alignment and the measurement of morphologic beat-to-beat variability in noisy signals , 2000, IEEE Transactions on Biomedical Engineering.

[82]  Peter Tiño,et al.  Minimum Complexity Echo State Network , 2011, IEEE Transactions on Neural Networks.

[83]  Philip Langley,et al.  Comparison of atrial signal extraction algorithms in 12-lead ECGs with atrial fibrillation , 2006, IEEE Transactions on Biomedical Engineering.

[84]  Philip Langley,et al.  Surface Atrial Frequency Analysis in Patients with Atrial Fibrillation: , 2004, Journal of cardiovascular electrophysiology.

[85]  Philip Langley,et al.  The U wave in atrial fibrillation , 2015, 2015 Computing in Cardiology Conference (CinC).

[86]  Shamim Nemati,et al.  Semisupervised ECG Ventricular Beat Classification With Novelty Detection Based on Switching Kalman Filters , 2015, IEEE Transactions on Biomedical Engineering.

[87]  Frida Sandberg,et al.  Noninvasive Assessment of Atrioventricular Nodal Function: Effect of Rate‐Control Drugs during Atrial Fibrillation , 2015, Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc.

[88]  Jorge Mateo Satos,et al.  Radial basis function neural networks applied to efficient QRST cancellation in atrial fibrillation , 2013, Comput. Biol. Medicine.

[89]  L J Haywood,et al.  New technique for detection of changes in QRS morphology of ECG signals. , 1983, The American journal of physiology.

[90]  Donald E. Gustafson,et al.  ECG/VCG Rhythm Diagnosis Using Statistical Signal Analysis-I. Identification of Persistent Rhythms , 1978, IEEE Transactions on Biomedical Engineering.

[91]  Pablo Laguna,et al.  A dynamic model to characterize beat-to-beat adaptation of repolarization to heart rate changes , 2008, Biomed. Signal Process. Control..

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

[93]  Pablo Laguna,et al.  Techniques for Ventricular Repolarization Instability Assessment From the ECG , 2016, Proceedings of the IEEE.

[94]  Roberto Sassi,et al.  A Signal Decomposition Model-Based Bayesian Framework for ECG Components Separation , 2016, IEEE Transactions on Signal Processing.

[95]  José Millet-Roig,et al.  Atrial activity extraction from atrial fibrillation episodes based on maximum likelihood source separation , 2005, Signal Process..

[96]  Ronald Phlypo,et al.  Atrial activity estimation from atrial fibrillation ECGs by blind source extraction based on a conditional maximum likelihood approach , 2010, Medical & Biological Engineering & Computing.

[97]  M Rahimpour,et al.  P wave detection in ECG signals using an extended Kalman filter: an evaluation in different arrhythmia contexts , 2016, Physiological measurement.

[98]  E. Basar,et al.  Wavelet entropy: a new tool for analysis of short duration brain electrical signals , 2001, Journal of Neuroscience Methods.

[99]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[100]  Herbert Jaeger,et al.  Reservoir computing approaches to recurrent neural network training , 2009, Comput. Sci. Rev..

[101]  David S. Rosenbaum,et al.  Frequency Based Measures Of Atrial Fibrillation In Man , 1990, [1990] Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[102]  Pablo Laguna,et al.  Principal Component Analysis in ECG Signal Processing , 2007, EURASIP J. Adv. Signal Process..

[103]  Chun-Li Wang,et al.  Single-lead f-wave extraction using diffusion geometry , 2017, Physiological measurement.

[104]  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.

[105]  Leif Sörnmo,et al.  Rapid slowing of the atrial fibrillatory rate after administration of AZD7009 predicts conversion of atrial fibrillation. , 2014, Journal of electrocardiology.

[106]  David H. Schoellhamer,et al.  Singular spectrum analysis for time series with missing data , 2001 .

[107]  A. Sahakian,et al.  Diagnosis of atrial fibrillation from surface electrocardiograms based on computer-detected atrial activity. , 1992, Journal of electrocardiology.

[108]  Jean-Francois Cardoso,et al.  Blind signal separation: statistical principles , 1998, Proc. IEEE.

[109]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[110]  L. Sörnmo,et al.  Detection of autonomic modulation in permanent atrial fibrillation , 2006, Medical and Biological Engineering and Computing.

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

[112]  Arnljot Tveit,et al.  Fibrillatory rate response to candesartan in persistent atrial fibrillation. , 2008, 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.

[113]  Nicolas Derval,et al.  Clinical value of fibrillatory wave amplitude on surface ECG in patients with persistent atrial fibrillation , 2009, Journal of Interventional Cardiac Electrophysiology.

[114]  José Millet-Roig,et al.  Optimal Localization of Leads in Atrial Fibrillation Episodes , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[115]  A. Oosterom.  The dominant T wave. , 2004 .

[116]  Lang Tong,et al.  Indeterminacy and identifiability of blind identification , 1991 .

[117]  A. Bollmann,et al.  Frequency analysis of human atrial fibrillation using the surface electrocardiogram and its response to ibutilide. , 1998, The American journal of cardiology.

[118]  Pierre Comon,et al.  A Contrast for Independent Component Analysis With Priors on the Source Kurtosis Signs , 2008, IEEE Signal Processing Letters.

[119]  M E Nygårds,et al.  An automated system for ECG monitoring. , 1979, Computers and biomedical research, an international journal.

[120]  Mohammad Bagher Shamsollahi,et al.  ECG Denoising and Compression Using a Modified Extended Kalman Filter Structure , 2008, IEEE Transactions on Biomedical Engineering.

[121]  Jie Lian,et al.  A simple method to quantify the morphological similarity between signals , 2010, Signal Process..

[122]  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.

[123]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[124]  César Sánchez,et al.  Convolutive Blind Source Separation Algorithms Applied to the Electrocardiogram of Atrial Fibrillation: Study of Performance , 2007, IEEE Transactions on Biomedical Engineering.

[125]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[126]  Kyoung-Joung Lee,et al.  Event synchronous adaptive filter based atrial activity estimation in single-lead atrial fibrillation electrocardiograms , 2012, Medical & Biological Engineering & Computing.

[127]  Andrzej Cichocki,et al.  Blind source extraction based on a linear predictor , 2007 .

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

[129]  Katerina Hnatkova,et al.  Optimal lead configuration in the detection and subtraction of QRS and T wave templates in atrial fibrillation , 1998, Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292).

[130]  Fernando S. Schlindwein,et al.  Analysis of QRS-T subtraction in unipolar atrial fibrillation electrograms , 2013, Medical & Biological Engineering & Computing.

[131]  Ye Li,et al.  QRS residual removal in atrial activity signals extracted from single lead: a new perspective based on signal extrapolation , 2016, IET Signal Process..

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

[133]  José Luis Rojo-Álvarez,et al.  Long-term characterization of persistent atrial fibrillation: wave morphology, frequency, and irregularity analysis , 2014, Medical & Biological Engineering & Computing.