Abstract
The development of non-invasive tools able to provide valuable information about the effectiveness of a shock in external electrical cardioversion (ECV) is clinically relevant to enhance these protocols in the treatment of atrial fibrillation (AF). The present contribution analyzes the ability of a non-linear regularity index, such as sample entropy (SampEn), to follow-up non-invasively AF organization under successive attempts of ECV and to predict the effectiveness of every single shock. To this respect, the atrial activity (AA) preceding each delivered shock was extracted by using a QRST cancellation method. Next, the main atrial wave (MAW), which can be considered as the fundamental waveform associated to the AA, was obtained by applying a selective filtering centered on the dominant atrial frequency (DAF). Finally, the MAW organization was estimated with SampEn and two thresholds (Th1 = 0.1223 and Th2 = 0.0832) were established to predict the ECV outcome. Results indicated that, prior to the first attempt, all the patients who needed only one shock to restore NSR were below Th1. In addition, most of them were above Th2 in case of AF relapsing during the first month. Regarding several shocks, all the patients who maintained NSR more than one month were below Th2 after the first shock. Moreover, all the patients who relapsed to AF during the first month were between Th1 and Th2 and, finally, all the patients with ineffective ECV were above Th1. After each unsuccessful shock, a SampEn relative decrease was observed for the patients who finally reverted to NSR, but the largest variation took place after the first attempt, thus indicating that this shock plays the most important role in the procedure. Indeed, by considering jointly the patients who needed only one shock and the patients who needed several shocks, 91.67% (22 out of 24) of ECVs resulting in NSR, 93.55% (29 out of 31) of ECVs relapsing to AF during the first month and 100% (10 out of 10) of ECVs in which NSR was not restored were correctly classified. As conclusion, the MAW organization analysis via SampEn can provide useful information that could improve the effectiveness of conventional external ECV protocols used in AF treatment.
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References
Alcaraz R, Rieta JJ (2009) A novel application of sample entropy to the electrocardiogram of atrial fibrillation. Nonlinear analysis: real world applications. doi:10.1016/j.nonrwa.2009.01.047
Alcaraz R, Rieta JJ (2009) Sample entropy of the main atrial wave predicts spontaneous termination of paroxysmal atrial fibrillation. Med Eng Phys. doi:10.1016/j.medengphy.2009.05.002
Alcaraz R, Rieta JJ (2008) A non-invasive method to predict electrical cardioversion outcome of persistent atrial fibrillation. Med Biol Eng Comput 46(7):625–635
Alcaraz R, Rieta JJ (2008) Wavelet bidomain sample entropy analysis to predict spontaneous termination of atrial fibrillation. Physiol Meas 29(1):65–80
Alcaraz R, Rieta JJ (2008) Adaptive singular value cancelation of ventricular activity in single-lead atrial fibrillation electrocardiograms. Physiol Meas 29(12):1351–1369
Alcaraz R, Rieta JJ (2009) Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation. Comput Methods Programs Biomed 93(2):148–154
Alcaraz R, Rieta JJ, Hornero F (2008) Atrial activity non-invasive characterization in previous instants before paroxysmal atrial fibrillation termination. Rev Esp Cardiol 61(2):154–160
Allessie MA, Konings K, Kirchhof CJ, Wijffels M (1996) Electrophysiologic mechanisms of perpetuation of atrial fibrillation. Am J Cardiol 77(3):10A–23A
Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D (1998) Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation 98(10):946–952
Berg MPVD, Noord TV, Brouwer J, Haaksma J, Veldhuisen DJV, Crijns HJGM, Gelder ICV (2004) Clustering of RR intervals predicts effective electrical cardioversion for atrial fibrillation. J Cardiovasc Electrophysiol 15(9):1027–1033
Bollmann A, Mende M, Neugebauer A, Pfeiffer D (2002) Atrial fibrillatory frequency predicts atrial defibrillation threshold and early arrhythmia recurrence in patients undergoing internal cardioversion of persistent atrial fibrillation. Pacing Clin Electrophysiol 25(8):1179–1184
Bollmann A, Husser D, Mainardi L, Lombardi F, Langley P, Murray A, Rieta JJ, Millet J, Olsson SB, Stridh M, Sörnmo L (2006) Analysis of surface electrocardiograms in atrial fibrillation: techniques, research, and clinical applications. Europace 8(11):911–926
Boos C, Thomas MD, Jones A, Clarke E, Wilbourne G, More RS (2003) Higher energy monophasic DC cardioversion for persistent atrial fibrillation: is it time to start at 360 joules? Ann Noninvasive Electrocardiol 8(2):121–126
Botteron GW, Smith JM (1995) A technique for measurement of the extent of spatial organization of atrial activation during atrial fibrillation in the intact human heart. IEEE Trans Biomed Eng 42(6):579–586
Botteron GW, Smith JM (1996) Quantitative assessment of the spatial organization of atrial fibrillation in the intact human heart. Circulation 93(3):513–518
Calcagnini G, Censi F, Michelucci A, Bartolini P (2006) Descriptors of wavefront propagation. Endocardial mapping of atrial fibrillation with basket catheter. IEEE Eng Med Biol Mag 25(6):71–78
Censi F, Barbaro V, Bartolini P, Calcagnini G, Michelucci A, Cerutti S (2001) Non-linear coupling of atrial activation processes during atrial fibrillation in humans. Biol Cybern 85(3):195–201
Dotsinsky I, Stoyanov T (2004) Optimization of bi-directional digital filtering for drift suppression in electrocardiogram signals. J Med Eng Technol 28(4):178–180
Everett TH, Kok LC, Vaughn RH, Moorman JR, Haines DE (2001) Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Trans Biomed Eng 48(9):969–978
Fuster V, Rydén LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA et al (2006) ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the european society of cardiology committee for practice guidelines (writing committee to revise the 2001 guidelines for the management of patients with atrial fibrillation): developed in collaboration with the european heart rhythm association and the heart rhythm society. Circulation 114(7):e257–e354
Gall NP, Murgatroyd FD (2007) Electrical cardioversion for AF-the state of the art. Pacing Clin Electrophysiol 30(4):554–567
Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE (2001) Prevalence of diagnosed atrial fibrillation in adults: national implications for rhythm management and stroke prevention: the AnTicoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study. JAMA 285(18):2370–2375
Govindan R, Wilsona J, Eswaranb H, Loweryb C, Preialb H (2007) Revisiting sample entropy analysis. Physica A Stat Mech Appl 376:158–164
Hoekstra BP, Diks CG, Allessie MA, DeGoede J (1995) Nonlinear analysis of epicardial atrial electrograms of electrically induced atrial fibrillation in man. J Cardiovasc Electrophysiol 6(6):419–440
Holm M, Pehrson S, Ingemansson M, Sörnmo L, Johansson R, Sandhall L, Sunemark M, Smideberg B, Olsson C, Olsson SB (1998) Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method. Cardiovasc Res 38(1):69–81
Holmqvist F, Stridh M, Waktare JEP, Roijer A, Sörnmo L, Platonov PG, Meurling CJ (2006) Atrial fibrillation signal organization predicts sinus rhythm maintenance in patients undergoing cardioversion of atrial fibrillation. Europace 8(8):559–565
Holmqvist F, Stridh M, Waktare JEP, Sörnmo L, Olsson SB, Meurling CJ (2006) Atrial fibrillatory rate and sinus rhythm maintenance in patients undergoing cardioversion of persistent atrial fibrillation. Eur Heart J 27(18):2201–2207
Husser D, Stridh M, Cannom DS, Bhandari AK, Girsky MJ, Kang S, Sörnmo L, Olsson SB, Bollmann A (2007) Validation and clinical application of time-frequency analysis of atrial fibrillation electrocardiograms. J Cardiovasc Electrophysiol 18(1):41–46. doi:10.1111/j.1540-8167.2006.00683.x
Joglar JA, Hamdan MH, Ramaswamy K, Zagrodzky JD, Sheehan CJ, Nelson LL, Andrews TC, Page RL (2000) Initial energy for elective external cardioversion of persistent atrial fibrillation. Am J Cardiol 86(3):348–350
Kim SS, Knight BP (2009) Electrical and pharmacologic cardioversion for atrial fibrillation. Cardiol Clin 27(1):95–107. doi:10.1016/j.ccl.2008.09.008
Konings K, Kirchhof C, Smeets J, Wellens H, Penn O, Allessie M (1994) High-density mapping of electrically induced atrial fibrillation in humans. Circulation 89:1665–1680
Lombardi F, Colombo A, Basilico B, Ravaglia R, Garbin M, Vergani D, Battezzati PM, Fiorentini C (2001) Heart rate variability and early recurrence of atrial fibrillation after electrical cardioversion. J Am Coll Cardiol 37(1):157–162
Mainardi LT, Porta A, Calcagnini G, Bartolini P, Michelucci A, Cerutti S (2001) Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes. Med Biol Eng Comput 39(2):249–254
Maixent JM, Barbey O, Pierre S, Duran MJ, Sennoune S, Bourdeaux M, Ricard P, Levy S (2000) Inhibition of Na,K-ATPase by external electrical cardioversion in a sheep model of atrial fibrillation. J Cardiovasc Electrophysiol 11(4):439–445
Martens SMM, Mischi M, Oei SG, Bergmans JWM (2006) An improved adaptive power line interference canceller for electrocardiography. IEEE Trans Biomed Eng 53(11):2220–2231
Miyasaka Y, Barnes ME, Gersh BJ, Cha SS, Bailey KR, Abhayaratna WP, Seward JB, Tsang TSM (2006) Secular trends in incidence of atrial fibrillation in Olmsted County, Minnesota, 1980 to 2000, and implications on the projections for future prevalence. Circulation 114(2):119–125
Nilsson F, Stridh M, Bollmann A, Sörnmo L (2006) Predicting spontaneous termination of atrial fibrillation using the surface ECG. Med Eng Phys 28(8):802–808
Pálinkás A, Antonielli E, Picano E, Pizzuti A, Varga A, Nyúzó B, Alegret JM, Bonzano A, Tanga M, Coppolino A, Forster T, Baralis G, Delnevo F, Csanády M (2001) Clinical value of left atrial appendage flow velocity for predicting of cardioversion success in patients with non-valvular atrial fibrillation. Eur Heart J 22(23):2201–2208
Petrutiu S, Ng J, Nijm GM, Al-Angari H, Swiryn S, Sahakian AV (2006) Atrial fibrillation and waveform characterization. A time domain perspective in the surface ECG. IEEE Eng Med Biol Mag 25(6):24–30
Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Natl Acad Sci USA 88(6):2297–2301
Pincus SM (2001) Assessing serial irregularity and its implications for health. Ann N Y Acad Sci 954:245–267
Prakash A, Saksena S, Krol RB, Philip G (2001) Right and left atrial activation during external direct-current cardioversion shocks delivered for termination of atrial fibrillation in humans. Am J Cardiol 87(9):1080–1088
Prystowsky EN (2008) The history of atrial fibrillation: the last 100 years. J Cardiovasc Electrophysiol 19(6):575–582
Raitt MH, Volgman AS, Zoble RG, Charbonneau L, Padder FA, O’Hara GE, Kerr D, Investigators AFFIRM (2006) Prediction of the recurrence of atrial fibrillation after cardioversion in the Atrial Fibrillation Follow-up Investigation of Rhythm Management (AFFIRM) Study. Am Heart J 151(2):390–396
Raitt MH, Kusumoto W, Giraud GD, McAnulty JH (2004) Electrophysiologic predictors of the recurrence of persistent atrial fibrillation within 30 days of cardioversion. Am J Cardiol 93(1):107–110
Ramdani S, Bouchara F, Lagarde J (2009 Influence of noise on the sample entropy algorithm. Chaos 19(1):013123
Rich MW (2009) Epidemiology of atrial fibrillation. J Interv Card Electrophysiol 25(1):3–8
Richman JS, Moorman JR (2000) Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol 278(6):H2039–H2049
Rieta JJ, Castells F, Sánchez C, Zarzoso V, Millet J (2004) Atrial activity extraction for atrial fibrillation analysis using blind source separation. IEEE Trans Biomed Eng 51(7):1176–1186
Sih HJ, Zipes DP, Berbari EJ, Olgin JE (1999) A high-temporal resolution algorithm for quantifying organization during atrial fibrillation. IEEE Trans Biomed Eng 46(4):440–450
Sörnmo L, Laguna P (2005) Bioelectrical signal processing in cardiac and neurological applications. Elsevier Academic Press
Stridh M, Sörnmo L, Meurling CJ, Olsson SB (2001) Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties. IEEE Trans Biomed Eng 48(1):19–27
Watson JN, Addison PS, Uchaipichat N, Shah AS, Grubb NR (2007) Wavelet transform analysis predicts outcome of DC cardioversion for atrial fibrillation patients. Comput Biol Med 37(4):517–523
Zohar P, Kovacic M, Brezocnik M, Podbregar M (2005) Prediction of maintenance of sinus rhythm after electrical cardioversion of atrial fibrillation by non-deterministic modelling. Europace 7(5):500–507
Acknowledgments
This work was supported by the projects TEC2007-64884 from the Spanish Ministry of Science and Innovation, PII2C09-0224-5983 from Junta de Comunidades de Castilla La Mancha and PAID-05-08 from Universidad Politécnica de Valencia.
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Alcaraz, R., Rieta, J.J. & Hornero, F. Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion. Med Biol Eng Comput 47, 1247–1255 (2009). https://doi.org/10.1007/s11517-009-0519-z
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DOI: https://doi.org/10.1007/s11517-009-0519-z