Resumen de Intracardiac signal processing for mapping and characterising cardiac arrhythmias
Cardiovascular disease is the principal cause of death worldwide [1], especially in the US and Europe [2], [3]. Some of these diseases express as arrhythmic events, i.e., alterations (either by acceleration or deceleration) of the normal sinus rhythm of the heart.
Arrhythmia treatment varies depending on the type of arrhythmia and patient characteristics. Anti-arrhythmic drug therapy is often the first choice for arrhythmia management, however catheter ablation is becoming a first line therapy specially in those cases when the disease is on its first states or the arrhythmia focus is identifiable [4], [5]. Catheter ablation is a minimally invasive clinical procedure that deploys small catheters in the cardiac chambers either on the endocardium via femoral vein and/or retrograde aorta access; or on the epicardium via subxiphoid puncture access [6]. Those catheters are equipped with electrodes that sense the local electrical activity of the heart, known as electrograms (EGM), during arrhythmia or sinus rhythm. Based on the characteristics of this electrical activity, radio frequency (RF) energy is delivered at those sites responsible of the clinical arrhythmia.
The aim of this thesis is to investigate signal processing techniques of invasive EGM signals in order to provide useful tools for helping in the decision process during ablation procedures and electrophysiological studies.
Electroanatomical mapping (EAM) systems are commonly used in catheter ablation interventions since they provide a 3D localization of the catheters within the patient chest minimizing the fluoroscopy exposure of both patients and electrophysiologists [7]. Moreover, EAM systems display clinical information relevant to the electrophysiologist in a 3D reconstruction of the patient's heart. EAM systems are especially useful for the treatment of stable and/or haemodynamically tolerated arrhythmias as atrial tachycardia or ventricular tachycardia (VT)[6]. However these systems do not include convenient EGM signal processing techniques and forces the system operator to manually modify the relevant measurements during the procedure. This increases the interpretation workload of the electrophysiologist performing during the ablation procedure. Therefore, these facts yield in very subject-dependent and time-consuming tasks done during stress situations which can comprise patient safety and intervention outcomes.
This problem is addressed in chapter 2. In this chapter, an automatic EGM delineator based on the wavelet transform of the bipolar electrogram (b-EGM) signal envelope is introduced and validated. It is motivated by the need of an operator-independent algorithm to measure local activation times (LATs) and to build ventricular activation maps in EAM systems. The accuracy of the method is first validated against manual annotations performed by two experts, showing errors in the same order as the annotations performed manually during the interventions in the electrophysiology lab. A second study assesses the ability of the method to generate activation maps in order to identify the area of interest for ablation and to determine the site of origin (SOO) in patients with idiopathic outflow tract ventricular arrhythmias (OTVAs). Automatic activation maps were compared with manual maps created during the procedure, showing similar accuracy in identifying the earliest activation area for ablation purposes and a slightly superior performance in the SOO identification of idiopathic OTVAs. Therefore, these results suggest that the proposed automatic b-EGM delineator may potentially help to reduce mapping acquisition time, especially when multi-electrode catheters are used, to reduce operator variability and to increase ablation outcomes.
On the other hand, EAM systems are not as useful for mapping unstable arrhythmias like atrial fibrillation (AF)[8]. AF is one of the most common arrhythmia in clinical practice[9], [10] accounting for more than a third of the hospitalizations in cardiac arrhythmias unit [2], [11]. Although many mechanisms have been proposed for the maintenance and perpetuation of AF (e.g. in[8], [12]–[18]), this arrhythmia is still incompletely understood [8]. Activation mapping can help to visualize and characterize the mechanisms that sustains and perpetuates the arrhythmia[19]. However, simultaneous recording of the electrical activity using multi-electrode catheters lack spatial resolution due to electrode sparsity, therefore high-density maps are desirable but limited nowadays to experimental epicardial studies [8], [19].
In chapter 3, a novel spatiotemporal approach to resolve high-density cardiac activation during AF using multi-electrode array (MEA) sensors is introduced and evaluated. Classic activation detection approaches from unipolar electrogram (u-EGM) signals simplifies the signal information to just a time-occurrence signal, rejecting the remaining spatiotemporal information embedded in the shape of u-EGMs. The method was evaluated against audited annotations from an expert electrophysiologist of recordings using a MEA sensor from a single patient ongoing open chest surgery during sinus rhythm and AF. The method provides smooth and accurate activation maps compared with the expert annotations and also provides “loci maps” that allow to visualize information regarding the number of concomitant wavefronts underpassing the MEA sensor and substrate properties. These results open the possibility of robust analysis of high-density activations maps and the development of minimally invasive high-density mapping.
Pulmonary vein isolation is the elective ablation approach for treating AF when anti-arrhythmic drug therapy is ineffective [10]. Normally, pulmonary vein isolation is complemented with targeting complex fractionated atrial sites [20], [21], atrial sites with higher dominant frequency [22] or lower organization [23]. Therefore, multiple EGM signal processing techniques have been proposed to provide tools that may help physicians to visualize these data in order to better guide the ablation procedure [24]. However, most of these signal processing techniques rely on the activation detection accuracy and/or does not take into consideration the spatiotemporal relation existing between close electrodes.
This problem is addressed in chapter 4. In this chapter, a predictability framework for analysis of AF activity, based on the concept of the Granger causality (GC), is introduced and evaluated. It is motivated by the fact that some EGM-based quantitative approaches for AF analysis rely on activation detection outcomes and/or do not take into consideration the spatiotemporal relation existing between close electrodes. The framework is evaluated in seven different simulation scenarios, which cover a wide range of propagation patterns, and in mapping data from patients showing different spatiotemporal patterns of AF. The proposed GC-based indices are able to differentiate the stability of the activation and can be combined for obtaining activity maps showing, in a single graph, the propagation of the activation and the stability of the cardiac substrate. Therefore, the proposed GC-based predictability framework is able to characterize and identify areas of AF instability and to track propagation, thus being able to guide the ablation procedure; it does not require activation detection or post-processing algorithms and it is applicable to any multi-electrode catheter.
In conclusion, a set of tools based on signal processing of intracardiac EGM signals for mapping and characterising cardiac arrhythmias has been proposed and validated in this thesis. Each contribution is oriented within a spatiotemporal approach, providing solutions that take into consideration both the specific problem as well as the complexity of cardiac arrhythmias.
Bibliography [1] R. Lozano et al., “Global and Regional Mortality from 235 Causes of Death for 20 Age Groups in 1990 and 2010: A Systematic Analysis for the Global Burden of Disease Study 2010,” Lancet, vol. 380, no. 9859, pp. 2095–2128, 2012.
[2] D. Mozaffarian et al., “Heart Disease and Stroke Statistics - 2015 Update: A Report From the American Heart Association,” Circulation, vol. 131, no. 4, pp. e29–e322, 2015.
[3] N. Townsend, M. Nichols, P. Scarborough, and M. Rayner, “Cardiovascular Disease in Europe -- Epidemiological Update 2015,” Eur. Heart J., p. ehv428, 2015.
[4] C. T. January et al., “2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation: Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society.,” J. Am. Coll. Cardiol., vol. 64, no. 21, pp. 2246–2280, 2014.
[5] S. G. Priori et al., “2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the Europe,” Eur. Heart J., vol. 36, no. 41, pp. 2793–2867, 2015.
[6] Z. F. Issa, J. M. Miller, and D. P. Zipes, Clinical Arrhythmology and Electrophysiology: A Companion to Braunwald’s Heart Disease, 2nd ed. Saunders, 2012.
[7] L. Gepstein, G. Hayam, and S. A. Ben-Haim, “A Novel Method for Nonfluoroscopic Catheter-Based Electroanatomical Mapping of the Heart. In Vitro and In Vivo Accuracy Results,” Circulation, vol. 95, no. 6, pp. 1611–1622, 1997.
[8] J. Eckstein, M. Kühne, S. Osswald, and U. Schotten, “Mapping of Atrial Fibrillation - Basic Research and Clinical Applications,” Swiss Med. Wkly., vol. 139, no. 35–36, pp. 496–504, 2009.
[9] V. Fuster et al., “ACC/AHA/ESC 2006 Guidelines for the Management of Patients with Atrial Fibrillation,” J. Am. Coll. Cardiol., vol. 48, no. 4, pp. e149–e246, 2006.
[10] A. J. Camm et al., “Guidelines for the Management of Atrial Fibrillation: The Task Force for the Management of Atrial Fibrillation of the European Society of Cardiology (ESC),” Eur. Heart J., vol. 31, no. 19, pp. 2369–2429, 2010.
[11] J. Friberg, B. Pernille, H. Scharling, N. Gadsbøll, and G. B. Jensen, “Rising Rates of Hospital Admissions for Atrial Fibrillation,” Epidemiology, vol. 14, pp. 666–672, 2003.
[12] G. K. Moe and J. A. Abildskov, “Atrial Fibrillation as a Self-Sustaining Arrhythmia Independent of Focal Discharge,” Am. Heart J., vol. 58, no. 1, pp. 59–70, 1959.
[13] M. A. Allessie, W. J. E. P. Lammers, F. I. M. Bonke, and J. Hollen, “Experimental Evaluation of Moe’s Multiple Wavelet Hypothesis of Atrial Fibrillation,” in Cardiac Electrophysiology and Arrhythmias, D. P. Zipes and J. Jalife, Eds. Grune & Stratton, 1985, pp. 265–276.
[14] K. T. Konings, C. J. Kirchhof, J. L. R. M. Smeets, H. J. Wellens, O. C. Penn, and M. A. Allessie, “High-Density Mapping of Electrically Induced Atrial Fibrillation in Humans,” Circulation, vol. 89, no. 4, pp. 1665–1680, 1994.
[15] M. Haïssaguerre et al., “Spontaneous Initiation of Atrial Fibrillation by Ectopic Beats Originating in the Pulmonary Veins,” N. Engl. J. Med., vol. 339, no. 10, pp. 659–666, 1998.
[16] G. Lee et al., “Epicardial Wave Mapping in Human Long-Lasting Persistent Atrial Fibrillation: Transient Rotational Circuits, Complex Wavefronts, and Disorganized Activity,” Eur. Heart J., vol. 35, no. 2, pp. 86–97, 2014.
[17] M. A. Allessie et al., “Electropathological Substrate of Long-Standing Persistent Atrial Fibrillation in Patients with Structural Heart Disease: Longitudinal Dissociation,” Circ. Arrhythmia Electrophysiol., vol. 3, no. 6, pp. 606–615, 2010.
[18] N. M. S. de Groot et al., “Electropathological Substrate of Longstanding Persistent Atrial Fibrillation in Patients with Structural Heart Disease: Epicardial Breakthrough,” Circulation, vol. 122, no. 17, pp. 1674–1682, 2010.
[19] A. Yaksh et al., “Atrial Fibrillation: To Map or Not to Map?,” Netherlands Hear. J., vol. 22, no. 6, pp. 259–266, 2014.
[20] K. Nademanee et al., “A New Approach for Catheter Ablation of Atrial Fibrillation: Mapping of the Electrophysiologic Substrate,” J. Am. Coll. Cardiol., vol. 43, no. 11, pp. 2044–2053, 2004.
[21] K. Nademanee, E. Lockwood, N. Oketani, and B. Gidney, “Catheter Ablation of Atrial Fibrillation Guided by Complex Fractionated Atrial Electrogram Mapping of Atrial Fibrillation Substrate,” J. Cardiol., vol. 55, no. 1, pp. 1–12, 2010.
[22] P. Sanders et al., “Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans,” Circulation, vol. 112, no. 6, pp. 789–797, 2005.
[23] T. H. Everett, L. C. Kok, R. H. Vaughn, J. R. Moorman, and D. E. Haines, “Frequency Domain Algorithm for Quantifying Atrial Fibrillation Organization to Increase Defibrillation Efficacy,” IEEE Trans. Biomed. Eng., vol. 48, no. 9, pp. 969–978, 2001.
[24] F. Ravelli and M. Masè, “Computational Mapping in Atrial Fibrillation: How the Integration of Signal-Derived Maps may Guide the Localization of Critical Sources,” Europace, vol. 16, no. 5, pp. 714–723, May 2014.
