Localization of Activation Origin on Patient-Specific Endocardial Surface by the Equivalent Double Layer (EDL) Source Model With Sparse Bayesian Learning.

OBJECTIVE: Ablation treatment of ventricular arrhythmias can be facilitated by pre-procedure planning aided by electrocardiographic inverse solution, which can help to localize the origin of arrhythmia. Our aim was to improve localization accuracy of the inverse solution for activation originating on the left-ventricular endocardial surface, by using a sparse Bayesian learning (SBL). METHODS: The inverse problem of electrocardiography was solved by reconstructing endocardial potentials from time integrals of body-surface electrocardiograms and from patient-specific geometry of the heart and torso for three patients with structurally normal ventricular myocardium, who underwent endocardial catheter mapping that included pace mapping. Complementary simulations using dipole sources in patient-specific geometry were also performed. The proposed method is using sparse property of the equivalent-double-layer (EDL) model of cardiac sources; it employs the SBL and makes use of the spatio-temporal features of the cardiac action potentials. RESULTS: The mean localization error of the proposed method for pooled pacing sites ( n=52) was significantly smaller ( p=0.0039) than that achieved for the same patients in the study of Erem et al. Simulation experiments localized the source dipoles ( n=48) from forward-simulated potentials with the error of 9.4 ± 4.5 mm (mean ± SD). CONCLUSION: The results of our clinical and simulation experiments demonstrate that localization of left-ventricular endocardial activation by means of the Bayesian approach, based on sparse representation of sources by EDL, is feasible and accurate. SIGNIFICANCE: The proposed approach to localizing endocardial sources may have important applications in pre-procedure assessment of arrhythmias and in guiding their ablation treatment.

Localization of ventricular activation origin using patient-specific geometry: Preliminary results.

BACKGROUND AND OBJECTIVES: Catheter ablation of ventricular tachycardia (VT) may include induction of VT and localization of VT-exit site. Our aim was to assess localization performance of a novel statistical pace-mapping method and compare it with performance of an electrocardiographic inverse solution. METHODS: Seven patients undergoing ablation of VT (4 with epicardial, 3 with endocardial exit) aided by electroanatomic mapping underwent intraprocedural 120-lead body-surface potential mapping (BSPM). Two approaches to localization of activation origin were tested: (1) A statistical method, based on multiple linear regression (MLR), which required only the conventional 12-lead ECG for a sufficient number of pacing sites with known origin together with patient-specific geometry of the endocardial/epicardial surface obtained by electroanatomic mapping; and (2) a classical deterministic inverse solution for recovering heart-surface potentials, which required BSPM and patient-specific geometry of the heart and torso obtained via computed tomography (CT). RESULTS: For the MLR method, at least 10-15 pacing sites with known coordinates, together with their corresponding 12-lead ECGs, were required to derive reliable patient-specific regression equations, which then enabled accurate localization of ventricular activation with unknown origin. For 4 patients who underwent epicardial mapping, the median of localization error for the MLR was significantly lower than that for the inverse solution (10.6 vs. 27.3 mm, P  =  0.034); a similar result held for 3 patients who underwent endocardial mapping (7.7 vs. 17.1 mm, P  =  0.017). The pooled localization error for all epicardial and endocardial sites was also significantly smaller for the MLR compared with the inverse solution (P  =  0.005). CONCLUSIONS: The novel pace-mapping approach to localizing the origin of ventricular activation offers an easily implementable supplement and/or alternative to the preprocedure inverse solution; its simplicity makes it suitable for real-time applications during clinical catheter-ablation procedures.

Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.

This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain.

Using transmural regularization and dynamic modeling for noninvasive cardiac potential imaging of endocardial pacing with imprecise thoracic geometry.

Cardiac electrical imaging from body surface potential measurements is increasingly being seen as a technology with the potential for use in the clinic, for example for pre-procedure planning or during-treatment guidance for ventricular arrhythmia ablation procedures. However several important impediments to widespread adoption of this technology remain to be effectively overcome. Here we address two of these impediments: the difficulty of reconstructing electric potentials on the inner (endocardial) as well as outer (epicardial) surfaces of the ventricles, and the need for full anatomical imaging of the subject's thorax to build an accurate subject-specific geometry. We introduce two new features in our reconstruction algorithm: a nonlinear low-order dynamic parameterization derived from the measured body surface signals, and a technique to jointly regularize both surfaces. With these methodological innovations in combination, it is possible to reconstruct endocardial activation from clinically acquired measurements with an imprecise thorax geometry. In particular we test the method using body surface potentials acquired from three subjects during clinical procedures where the subjects' hearts were paced on their endocardia using a catheter device. Our geometric models were constructed using a set of CT scans limited in axial extent to the immediate region near the heart. The catheter system provides a reference location to which we compare our results. We compare our estimates of pacing site localization, in terms of both accuracy and stability, to those reported in a recent clinical publication , where a full set of CT scans were available and only epicardial potentials were reconstructed.

Heart rate turbulence after ventricular pacing trains during programmed ventricular stimulation.

BACKGROUND: This study tested the hypothesis that heart rate turbulence (HRT) following ventricular pacing trains depends on train cycle length, presence of retrograde ventriculoatrial (VA) conduction, and left ventricular (LV) function. METHODS: We analyzed digital recordings of programmed ventricular stimulation (PVS) performed in 82 patients (57 men) referred for electrophysiologic studies of ventricular arrhythmias, whose mean age was 64 +/- 12 years and LV ejection fraction (EF) was 47 +/- 15%. Profiles of sinus RR intervals after all available 8-beat ventricular pacing trains (600-and 400-ms) were averaged. Heart rate turbulence slope (HRTS) was analyzed as the maximum positive slope of a regression line through a sequence of 2-5 (HRTS2 - HRTS5) consecutive RR intervals within the first 5 RR intervals after the pacing train. RESULTS: Dynamics of RR intervals had biphasic and monophasic patterns, in patients with and without VA conduction, respectively. Sinus nodal response was less prominent after 600-ms than 400-ms pacing trains. After 400-ms pacing trains, HRTS was significantly shallower in patients with LVEF 40%. HRTS4 was the best discriminator between the two groups (6.8 +/- 8.6 ms/RR vs 19.6 +/- 26.0 ms/RR, P = 0.017). CONCLUSION: In patients with VA conduction, HRT after ventricular pacing trains reflects a combination of vagal withdrawal due to transient hypotension and suppression of sinus node automaticity. Attenuation of vagal modulation was detected in patients with LV dysfunction during standard PVS.

Heart rate turbulence after atrial premature complexes depends on coupling interval and atrioventricular nodal conduction.

BACKGROUND: Positive Turbulence onset (TO) after atrial premature complexes (APCs) was found temporally related to spontaneous episodes of atrial fibrillation. This study tested the hypothesis that heart rate turbulence (HRT) after APCs is influenced by APC prematurity independently of the prematurity of conducted ventricular complexes. METHODS: We studied 33 patients (mean age = 58 +/- 16 years, 19 men), 11 of whom had structural heart disease, who were referred for electrophysiological studies of supraventricular or ventricular arrhythmias. Sequences of single right atrial extrastimuli were delivered with coupling intervals adjusted to reach 60% prematurity of conducted ventricular complexes. Descriptors of HRT were compared between patients with slow versus fast atrioventricular (AV) conduction of APCs. RESULTS: The early RR interval dynamics after APCs was prominently modulated by the suppression of sinus node automaticity by the direct effect of APCs. This effect was significantly greater after earlier APCs with longer AV conduction times than after later coupled APCs with shorter AV conduction times. CONCLUSIONS: The early phase of HRT is strongly influenced by the coupling interval of APCs, independently of the prematurity of conducted ventricular complexes. Consequently, the more positive TO preceding spontaneous atrial fibrillation episodes might be an epiphenomenon of incidental short-coupled APCs with delayed AV conduction, likely to trigger atrial fibrillation.

Temporal pattern of conduction recurrence during radiofrequency ablation for typical atrial flutter.

INTRODUCTION: Conduction recurrence during radiofrequency (RF) ablation of cavotricuspid isthmus for typical atrial flutter is common. Understanding the temporal pattern of recurrences could help to predict a durable bidirectional block (BDB) and optimize the procedure. METHODS AND RESULTS: We analyzed atrial flutter ablations in 108 consecutive patients (85 males, age 63 +/- 11 years). RF energy was delivered through 8-mm tip or 4-mm cooled-tip catheter. On average, 18 +/- 11 pulses were necessary to achieve BDB. The time to recurrence of conduction after RF cessation was recorded. Early and late conduction recurrences were defined as < or =10 minutes and >10 minutes, respectively. Patients were observed for > or =30 minutes after bidirectional cavotricuspid isthmus (CTI) block was achieved. Conduction did not recur in 46 patients. In 8 cases, no block was achieved. A total of 167 conduction recurrences were recorded in the remaining 54 cases (1-10 per case). Of these, in 53 patients, recurrences were classified as early (98%) and 14 patients had late recurrences (8%). Thirteen patients had both early and late recurrences (24%). All but one late recurrence were preceded by at least one early recurrence. Absence of early recurrence had negative predictive value of 98%, while any early recurrence had positive predictive value of 26% for subsequent late conduction recovery. CONCLUSION: Incidence of isthmus conduction recurrence rapidly decayed during the waiting period. Absence of conduction recurrence within 10 minutes after first successful RF delivery was highly predictive of persistent BDB.

QT dispersion in 120 electrocardiographic leads in patients with structural heart disease.

The clinical significance of QT dispersion (QTd) measured in 12-lead ECGs is controversial. The aim of this study was to clarify factors that determine the QTd and its measurement errors in different lead arrays in patients with structural heart disease. Two blinded observers measured QT intervals on a computer screen from 120-channel ECG recordings in a retrospective set of 257 patients, comprising a group of 121 myocardial infarction (MI) survivors without ventricular tachyarrhythmia during a 6-month follow-up and a group of 136 survivors of ventricular tachyarrhythmia/fibrillation. QTd did not differ in patients with and without ventricular tachyarrhythmia/fibrillation. Eleven ventricular tachyarrhythmia/fibrillation survivors without structural heart disease had the lowest QTd (P < or = 0.02). The strongest factor determining QTd and the magnitude of its measurement error was the lead array (P = 0.0001). Measurement errors had two components. The smallest relative errors were in the total body surface mapping array with one component related to interobserver reproducibility (9.1 +/- 7.6%), and the other component related to accuracy of measurement of the QT interval (36 +/- 16%). The authors estimated that a difference of QTd of at least 50 ms between study groups is required in a 12-lead ECG to draw any conclusions from the studies. In patients with structural heart disease, QTd from limited arrays of ECG leads was not a reliable measure. It correlated with the presence of structural heart disease, but not with arrhythmogenicity. An array consisting of ECG leads covering the entire chest allowed better reproducibility and measurement accuracy of QTd.