This study introduced a novel nonlinear technique to quantify the repetitiveness of consecutive fibrillation electrograms in patients with persistent AF, irrespective of the intervaldependent variables. Within the continuous CFEs, conventional linear signal analysis could not differentiate the termination sites from nontermination sites.
Targeting CFE sites in the presence of a high level of electrogram similarity of the fractionated electrograms in the LA was correlated with the procedural AF termination and freedom from AF recurrence after the index procedure. This study suggested that sites with a high level of fibrillation electrogram similarity at the CFEs are important for AF maintenance.
Study Limitations
First, the high SI sites outside the continuous CFEs were not targeted. However, this study demonstrated that most of the high SI sites were located within the continuous CFEs.
Second, the CFE ablation was not based on the SI. However, in this study, the continuous CFEs (mostly the highest similarity sites) were targeted first and procedural AF termination was the endpoint of procedure. After procedural termination, sites with lesser degree of continuous CFEs were not targeted. A recent study demonstrated that the proportion of the lesions causing termination was not affected by the order of ablating the targeted CFE sites.4
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REFERENCES
1. Nattel S: New ideas about atrial fibrillation 50 years on. Nature 2002;10;415:219-226.
2. Hayward RM, Upadhyay GA, Mela T, Ellinor PT, Barrett CD, Heist EK, Verma A, Choudhry NK, Singh JP: Pulmonary vein isolation with complex fractionated atrial electrogram ablation for paroxysmal and nonparoxysmal atrial fibrillation: A meta- analysis. Heart Rhythm 2011;8:994-1000.
3. Calkins H, Brugada J, Packer DL, Cappato R, Chen SA, Crijns HJ, Damiano RJ Jr, Davies DW, Haines DE, Haissaguerre M, Iesaka Y, Jackman W, Jais P, Kottkamp H, Kuck KH, Lindsay BD, Marchlinski FE, McCarthy PM, Mont JL, Morady F, Nademanee K, Natale A, Pappone C, Prystowsky E, Raviele A, Ruskin JN, Shemin RJ: HRS/EHRA/ECAS expert Consensus Statement on catheter and surgical ablation of atrial fibrillation: Recommendations for personnel, policy, procedures and follow-up. A report of the Heart Rhythm Society (HRS) Task Force on catheter and surgical ablation of atrial fibrillation. Heart Rhythm 2007;4:816-861.
4. Hunter RJ, Diab I, Tayebjee M, Richmond L, Sporton S, Earley MJ, Schilling RJ:
Characterization of fractionated atrial electrograms critical for maintenance of atrial fibrillation: A randomized, controlled trial of ablation strategies (the CFAE AF trial).
Circ Arrhythm Electrophysiol 2011;4:622-629.
5. Atienza F, Almendral J, Jalife J, Zlochiver S, Ploutz-Snyder R, Torrecilla EG, Arenal A, Kalifa J, Fern´andez-Avil´es F, Berenfeld O: Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. Heart Rhythm 2009;6:33-40.
6. Verma A, Novak P, Macle L, Whaley B, Beardsall M, Wulffhart Z, Khaykin Y: 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. Heart Rhythm 2008;5:198-205.
7. Nademanee K, McKenzie J, Kosar E, Schwab M, Sunsaneewitayakul B, Vasavakul T, Khunnawat C, Ngarmukos T: A new approach for catheter ablation of atrial fibrillation: Mapping of the electrophysiologic substrate. J Am Coll Cardiol 2004;43:2044-2053.
8. Alcaraz R, Rieta JJ: Non-linear organization analysis of paroxysmal atrial fibrillation. Conf Proc IEEE Eng Med Biol Soc 2007;2007:1957-1960.
9. Lo LW, Tai CT, Lin YJ, Chang SL, Wongcharoen W, Chang SH, Hsieh MH, Tuan TC, Udyavar AR, Chen YJ, Tsao HM, Chen SA: Progressive remodeling of the atrial substrate–a novel finding from consecutive voltage mapping in patients with recurrence of atrial fibrillation after catheter ablation. J Cardiovasc Electrophysiol 2007;18:258-265.
10. Chang SL, Tai CT, LinYJ,Wongcharoen W, Lo LW, Tuan TC, Udyavar AR, Chang
79
SH, Tsao HM, Hsieh MH, Hu YF, Chen YJ, Chen SA: The efficacy of inducibility and circumferential ablation with pulmonary vein isolation in patients with paroxysmal atrial fibrillation. J Cardiovasc Electrophysiol 2007;18:607-611.
11. Everett TH, Kok LC, Vaughn RH,Moorman JR, Haines DE: Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Trans Biomed Eng 2001;48:969-978.
12. Lin YJ, Tai CT, Kao T, Chang SL,Wongcharoen W, Lo LW, Tuan TC, Udyavar AR, Chen YJ, Higa S, Ueng KC, Chen SA: Consistency of complex fractionated atrial electrograms during atrial fibrillation. Heart Rhythm 2008;5:406-412.
13. Cheng F, Venetsanopoulos AN: An adaptive morphological filter for image processing. IEEE Trans Image Process 1992;1:533-539.
14. Lin YJ, Tai CT, Kao T, Chang SL, Lo LW, Tuan TC, Udyavar AR, Wongcharoen W, Hu YF, Tso HW, Tsai WC, Chang CJ, Ueng KC, Higa S, Chen SA:
Spatiotemporal organization of the left atrial substrate after circumferential pulmonary vein isolation of atrial fibrillation. Circ Arrhythm Electrophysiol 2009;2:233-241.
15. LinYJ, Tai CT, Chang SL, Lo LW, Tuan TC,Wongcharoen W, Udyavar AR, Hu YF, Chang CJ, Tsai WC, Kao T, Higa S, Chen SA: Efficacy of additional ablation of complex fractionated atrial electrograms for catheter ablation of nonparoxysmal atrial fibrillation. JCardiovasc Electrophysiol 2009;20:607-615.
16. Faes L, Nollo G, Antolini R, Gaita F, Ravelli F: A method for quantifying atrial fibrillation organization based on wave-morphology similarity. IEEE Trans Biomed Eng 2002;49:1504-1513.
17. Lo LW, Higa S, Lin YJ, Chang SL, Tuan TC, Hu YF, Tsai WC, Tsao HM, Tai CT, Ishigaki S, Oyakawa A, Maeda M, Suenari K, Chen SA: The novel electrophysiology of complex fractionated atrial electrograms: Insight from noncontact unipolar electrograms. J Cardiovasc Electrophysiol 2010;21:640-648.
18. Takahashi Y, O’Neill MD, Hocini M, Dubois R, Matsuo S, Knecht S, Mahapatra S, Lim KT, Ja¨ıs P, Jonsson A, Sacher F, Sanders P, Rostock T, Bordachar P, Cl´ementy J, Klein GJ, Ha¨ıssaguerre M: Characterization of electrograms associated with termination of chronic atrial fibrillation by catheter ablation. J AmColl Cardiol 2008;51:1003-1010.
19. Verma A, Wulffhart Z, Beardsall M, Whaley B, Hill C, Khaykin Y: Spatial and temporal stability of complex fractionated electrograms in patients with persistent atrial fibrillation over longer time periods: Relationship to local electrogram cycle length. Heart Rhythm 2008;5:1127-1133.
20. Stiles MK, Brooks AG, Kuklik P, John B, Dimitri H, Lau DH, Wilson L, Dhar S, Roberts-Thomson RL, Mackenzie L, Young GD, Sanders P: High-density mapping of atrial fibrillation in humans: Relationship between high-frequency activation and electrogram fractionation. J Cardiovasc Electrophysiol 2008;19:1245-1253.
21. Narayan SM, Wright M, Derval N, Jadidi A, Forclaz A, Nault I, Miyazaki S, Sacher F, Bordachar P, Cl´ementy J, Ja¨ıs P, Ha¨ıssaguerre M, Hocini M: Classifying
80
fractionated electrograms in human atrial fibrillation using monophasic action potentials and activation mapping: Evidence for localized drivers, rate acceleration, and nonlocal signal etiologies. Heart Rhythm 2011;8:244-253.
22. Chang SH, Ulfarsson M, Chugh A, Yoshida K, Jongnarangsin K, Crawford T, Good E, Pelosi F Jr, Bogun F, Morady F, Oral H: Time- and frequency-domain characteristics of atrial electrograms during sinus rhythm and atrial fibrillation. J Cardiovasc Electrophysiol 2011;22:851-857.
23. Kalifa J, Tanaka K, Zaitsev AV, Warren M, Vaidyanathan R, Auerbach D, Pandit S, Vikstrom KL, Ploutz-Snyder R, Talkachou A, Atienza F, Guiraudon G, Jalife J, Berenfeld O: Mechanisms of wave fractionation at boundaries of high-frequency excitation in the posterior left atrium of the isolated sheep heart during atrial fibrillation. Circulation 2006;113:626-633.
24. Lin YJ, Tsao HM, Chang SL, Lo LW, Hu YF, Chang CJ, Tsai WC, Suenari K, Huang SY, Chang HY, Wu TJ, Chen SA: The role of high dominant frequency sites in non-paroxysmal AF patients: Insights from high-density frequency and fractionation mapping. Heart Rhythm 2010;7:1255-1262.
25. Kester W: Mixed-Signal and DSP Design Techniques. Burlington, MA: Elsevier Science 2003; pp. 6.1-6.31.
26. Pallav P, Gan TH, Hutchins DA: Elliptical Tukey chirp signal for high-resolution, air-coupled ultrasonic imaging. IEEE Trans Ultrason Ferroelectr Freq Control 2007;54:1530-1540.
27. Ciaccio EJ, Biviano AB, Whang W, Gambhir A, Garan H. Spectral profiles of complex fractionated atrial electrograms are different in longstanding and acute onset atrial fibrillation atrial electrogram spectra. J Cardiovasc Electrophysiol 2012;23:971-979.
28. Everett TH, Kok LC, Vaughn RH,Moorman JR, Haines DE: Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Trans Biomed Eng 2001;48:969-978.
29. Ng J, Kadish AH, Goldberger JJ: Technical considerations for dominant frequency analysis. J Cardiovasc Electrophysiol 2007;18:757-764.
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Chapter 4 Conclusion
Physiological data, from simple to complex, as mentioned earlier, are usually considered as the results of a running dynamic systems. The normal healthy state of any living system is in homeostasis, which is not static, but dynamically change in time, which exhibits high degree of complexity and highly orders. For biomedical signal processing, such complex systems is difficult to deal with, and hard to be described through limited clinical data. In this point, complex theory has been used, many nonlinear methods were then proposed in recent years, trying to describe and identify such complex phenomena on limited data. Base on these, this dissertation proposed several robust methods on quantization the multi-scale correlation and attracting orbits, try to extend the usage of nonlinear methodology and fulfill certain requirements.
Quantization of Multi-scale correlation
The multi-scale correlation on reconstructed phase space can quantify the complexity of a time series, however, the coarse-graining steps will result in less number of point in larger scale for reliable calculation. By rearrange the coarse-graining steps, the data points on large scale can be extended and hence increase the reliability. The cost for this would be the larger volume of computation. The results shows, by utilize the novel sMSE approach for PWV signal analysis, the time for data acquisition can be substantially reduced from 30 minutes to 10 minutes with remarkable preservation of sensitivity in differentiating among the healthy, aged, and diabetic populations compared with the conventional MSE method.
On the other hand, sample-entropy based calculation of correlation need a criteria for neighborhood which is easy to be effected by extreme value of outliers, as well as the coarse-graining steps are easy to be interfered by frequency happen outliers. Therefore, all previous complexity analyses require heartbeat signals without outliers or ectopic beats. Removing outliers or ectopic beats is not trivial, not only requiring specific expertise in ECG waveforms but also being very time-consuming. However, how the automatic filtering affects complexity measures is not known. In addition, the procedure works well in the signals with the occasional and isolated ectopic beats but may not be applicable in the data with numerous and often continuous ectopic beats as occurred in ECG data of ECMO patients. By utilize the symbolic dynamics method and replace the mean with median on the coarse-graining steps, the results demonstrated the recursive
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automatic filtering only slightly attenuate the effect of outliers in MSE. Moreover, ectopic beats in certain patients such as the ECMO patients occur so frequently that no continuous heartbeat recordings can be obtained after removing all ectopic beats. The proposed MSSE was specially designed to resolve these problems. With its reliability and high resilience to outliers, the method gives the hope of applying the theory of complexity in clinical practice. Further validation of the method using a large sample size is warranted.
Quantification of Attracting Orbit
The attracting orbits reflect the real dynamics of a system, but the underlying rules are not easy to figure out. The Poincaré plot provides basic statistic information of cycle length which is important for HRV, however, need a method to identify each cycle at first. For the continuous monitoring FHR through HRV during CS, extracting from the maternal abdominal ECG remains a challenge since the electrodes cannot be placed properly. Moreover, the CS procedure would introduce large motion artifacts and myopotential interference which are difficult to deal with by using traditional methods.
The proposed algorithms successfully identify each cycle of heart beat and demonstrate that abnormal FHR during spinal or epidural anesthesia are primarily the results of uterine hypertonus and maternal hypotension. The FHR was significantly increased but remained within the normal range, which might be attributed to vasodilatation caused by spinal anesthesia. The effective data are difficult to obtain because of the high possibility of contamination and interference during the delivery. As a result, the study population was small which need a larger population study in the future that could validate the meaning of the derived parameters.
On the other hand, identify the periodic orbit cycle in very complex dynamics system, such as determine the LAWs of the fibrillation electrogram in AF patient, is hard and crucial. Through the proposed nonlinear method, the LAWs as well as each periodic cycle were identified. Moreover, through the correlations of each identified orbits, the percentage of LAWs pairs indicating how many pairs of orbits are similar, can be quantified as the similarity index (SI). It is worth noticing that the regularity, calculated by the conventional accumulation of each activated-wave deviation from the averaged template, could not differentiate the termination sites from nontermination sites. This study also suggested that sites with a high level of fibrillation electrogram similarity at the CFEs are important for AF maintenance.