Rotor pivot point identification using recurrence period density entropy

Catheter ablation to terminate atrial fibrillation (AF), a most common cardiac arrhythmia has been shown to be successful for paroxysmal AF patients. However, limitations exist with inadequate cardiac mapping systems for persistent AF patients to identify active substrates outside the pulmonary vein region. Previously, Shannon Entropy (SE) based mapping approach was proposed to identify regions of high SE and to generate patient specific three-dimensional SE maps using current catheter mapping system. However, the exact location of the pivot point of the rotor has not been correctly identified using this approach. In this work, we present robust recurrence period density entropy (RPDE) based approach accurately identify pivot point of the rotors that were induced in ex-vivo isolated rabbit heart. Our results demonstrate the efficacy of the RPDE approach to precisely identify the pivot point of the rotor, and to provide a better contrast between the rotor core and the periphery region when compared to SE approach. The results motivate further application and validation of this technology using intra-atrial electrograms from paroxysmal and persistent AF patients aiming to accurately identify the location of the rotor pivot point.

[1]  S. J. Roberts,et al.  Temporal and spatial complexity measures for electroencephalogram based brain-computer interfacing , 2006, Medical & Biological Engineering & Computing.

[2]  D. Lin,et al.  Long-Term Outcome After Successful Catheter Ablation of Atrial Fibrillation , 2010, Circulation. Arrhythmia and electrophysiology.

[3]  J. Kalman,et al.  Pulmonary Vein Antral Isolation for Paroxysmal Atrial Fibrillation: Results from Long‐Term Follow‐Up , 2010, Journal of cardiovascular electrophysiology.

[4]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[5]  I. Percival,et al.  A spectral entropy method for distinguishing regular and irregular motion of Hamiltonian systems , 1979 .

[6]  Omer Berenfeld,et al.  Presence and stability of rotors in atrial fibrillation: evidence and therapeutic implications. , 2016, Cardiovascular research.

[7]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[8]  Max A. Little,et al.  Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection , 2007 .

[9]  Shuen-De Wu,et al.  Modified multiscale entropy for short-term time series analysis , 2013 .

[10]  T. Chao,et al.  Clinical Outcome of Catheter Ablation in Patients With Nonparoxysmal Atrial Fibrillation: Results of 3-Year Follow-Up , 2012, Circulation. Arrhythmia and electrophysiology.

[11]  Wouter-Jan Rappel,et al.  Treatment of atrial fibrillation by the ablation of localized sources: CONFIRM (Conventional Ablation for Atrial Fibrillation With or Without Focal Impulse and Rotor Modulation) trial. , 2012, Journal of the American College of Cardiology.

[12]  Matthew Wright,et al.  Catheter ablation for atrial fibrillation: are results maintained at 5 years of follow-up? , 2011, Journal of the American College of Cardiology.

[13]  Shivaram P. Arunachalam,et al.  Feasibility of visualizing higher regions of Shannon entropy in atrial fibrillation patients , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  H. Fogedby On the phase space approach to complexity , 1992 .

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

[16]  Max A. Little,et al.  Nonlinear, Biophysically-Informed Speech Pathology Detection , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

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

[18]  Sanjiv M. Narayan,et al.  Mechanistically based mapping of human cardiac fibrillation , 2016, The Journal of physiology.

[19]  S. Pincus Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[20]  Kalyanam Shivkumar,et al.  Quantitative Analysis of Localized Sources Identified by Focal Impulse and Rotor Modulation Mapping in Atrial Fibrillation , 2015, Circulation. Arrhythmia and electrophysiology.

[21]  C. Murray,et al.  Worldwide Epidemiology of Atrial Fibrillation: A Global Burden of Disease 2010 Study , 2014, Circulation.

[22]  Mario Baldi,et al.  Radiofrequency Catheter Ablation and Antiarrhythmic Drug Therapy: A Prospective, Randomized, 4-Year Follow-Up Trial: The APAF Study , 2011, Circulation. Arrhythmia and electrophysiology.

[23]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[24]  Elena G. Tolkacheva,et al.  Optical mapping of electrical heterogeneities in the heart during global ischemia , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.