Investigation on recurrent high dominant frequency spatiotemporal patterns during persistent atrial fibrillation

Atrial regions hosting dominant frequency (DF) may represent potential drivers of persistent atrial fibrillation (persAF). Previous work showed that DF can exhibit cyclic behaviour. This study aims to better understand the spatiotemporal behaviours of persAF over longer time periods. 10 patients undergoing persAF ablation targeted at DF were included. Left atrial (LA) non-contact virtual electrograms (VEGMs, Ensite Array, St Jude Medical) were collected for up to 5 min pre-/post- ablation. DF was identified as the peak from 4-10 Hz, in 4 s windows (50 % overlap). High DF (HDF) map was created and automated pattern recognition algorithm was applied to look for recurring HDF spatial patterns within each patient. Recurring HDF patterns were found in all patients. Patients who changed rhythm to atrial flutter after ablation demonstrated single dominant pattern (DP) among the recorded time period, which might consistent with the higher level of regularity during flutter. Ablation regularized AF as demonstrated by increased DP recurrence after ablation. The time interval (median [IQR]) of DP recurrence for the patients still in atrial fibrillation(AF) after ablation (7 patients) decreased from 21.1 s [11.8~49.7 s] to 15.7s [6.5~18.2 s]. The proposed method quantifies the spatiotemporal regularity of HDF DPs over long time periods and may offer a more comprehensive dynamic overview of persAF behaviour and the impact of ablation

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Automatic extraction of recurrent patterns of high dominant frequency mapping during human persistent atrial fibrillation

Purpose: Identifying targets for catheter ablation remains challenging in persistent atrial fibrillation (persAF). The dominant frequency (DF) of atrial electrograms during atrial fibrillation (AF) is believed to primarily reflect local activation. Highest DF (HDF) might be responsible for the initiation and perpetuation of persAF. However, the spatiotemporal behaviour of DF remains not fully understood. Some DFs during persAF were shown to lack spatiotemporal stability, while others exhibit recurrent behaviour. We sought to develop a tool to automatically detect recurrent DF patterns in persAF patients. Methods: Non-contact mapping of the left atrium (LA) was performed in 10 patients undergoing persAF HDF ablation. 2048 virtual electrograms (vEGMs, EnSite Array, Abbott Laboratories, USA) were collected for up to 5 min before and after ablation. Frequency spectrum was estimated using fast Fourier transform and DF was identified as the peak between 4-10 Hz and organization index (OI) was calculated. The HDF maps were identified per 4-second window and an automated pattern recognition algorithm was used to find recurring HDF spatial patterns. Dominant patterns (DPs) were defined as the HDF pattern with the highest recurrence. Results: DPs were found in all patients. Patients in atrial flutter after ablation had a single DP over the recorded time period. The time interval (median [IQR]) of DP recurrence for the patients in AF after ablation (7 patients) decreased from 21.1 s [11.8 49.7s] to 15.7 s [6.5 18.2 s]. The DF inside the DPs presented lower temporal standard deviation (0.18±0.06 Hz vs. 0.29±0.12 Hz, p<0.05) and higher OI (0.35±0.03 vs. 0.31±0.04, p<0.05). The atrial regions with the highest proportion of HDF region were the septum and the left upper pulmonary vein. Conclusion: Multiple recurrent spatiotemporal HDF patterns exist during persAF. The proposed method can identify and quantify the spatiotemporal repetition of the HDFs, where the high recurrences of DP may suggest a more organised rhythm. DPs presented a more consistent DF and higher organisation compared with non-DPs, suggesting that DF with higher OI might be more likely to recur. Recurring patterns offer a more comprehensive dynamic insight of persAF behaviour, and ablation targeting such regions may be beneficial

Noninvasive Assessment of Spatio-Temporal Recurrence in Atrial Fibrillation

Propagation of Atrial Activity during atrial fibrillation (AF) is a complex phenomenon characterized by a certain degree of recurrence (periodic repetition). In this study, we investigated the possibility to detect recurrence noninvasively from body surface potential map recordings in patients affected by persistent AF, and localize this recurrence both in time and space. Results showed that clusters of recurrence can be identified from body surface recordings in these patients. Moreover, the number of clusters detected and their location on the top-right of the back of the torso were significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. This suggests that noninvasive quantification of recurrence in persistent AF patients is possible, and may contribute to improve patient stratification

Catheter ablation in patients with atrial fibrillation : mapping refinements, outcome prediction and effect on quality of life

PhD ThesisChapter 1 presents a literature review, focused primarily on the pathophysiology and management of atrial fibrillation (AF). Chapter 2 examines correlations between the dominant frequency of AF - calculated using principal component analysis from a modified surface 12-lead ECG (which included posterior leads), a standard 12-lead ECG and intracardiac recordings from both atria. The inclusion of posterior leads did not improve correlation with left atrial activity because of the dominance of lead V1 in both ECG configurations. Chapter 3 explores whether acute and 12-month outcome following catheter ablation for AF can be predicted beforehand from clinical and surface AF waveform parameters. Multivariate risk scores combining these parameters can predict arrhythmia outcome following ablation, and could therefore be used to identify those most likely to benefit from this therapy. Chapter 4 examines the effect of catheter ablation on AF symptoms and quality of life (QoL). AF symptom and QoL scores improved significantly in patients who maintained sinus rhythm after ablation but did not change in those with recurrent AF. AF-specific QoL scales are more responsive to change and correlate better with ablation outcome. Chapter 5 examines inter-atrial frequency gradients in patients with persistent AF using multipolar contact mapping. A right-to-left atrial frequency gradient was found in a quarter of the patients studied, implying that their arrhythmia was being maintained by high frequency sources in the right rather than the left atrium. Chapter 6 examines whether targeting high frequency and highly repetitive complex fractionated atrial electrogram sites, identified using multipolar contact mapping during persistent AF, resulted in arrhythmia termination and maintenance of sinus rhythm long-term. The utility of administering flecainide to distinguish critical from bystander AF sites was also investigated. Flecainide did not help refine ablation targets and 12-month outcome after targeting these sites was not superior to other ablation strategies

An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping

Background and Objective: Optimal targets for persistent atrial fibrillation (persAF) ablation are still debated. Atrial regions hosting high dominant frequency (HDF) are believed to participate in the initiation and maintenance of persAF and hence are potential targets for ablation, while rotor ablation has shown promising initial results. Currently, no commercially available system offers the capability to automatically identify both these phenomena. This paper describes an integrated 3D software platform combining the mapping of both frequency spectrum and phase from atrial electrograms (AEGs) to help guide persAF ablation in clinical cardiac electrophysiological studies. Methods: 30 s of 2048 non-contact AEGs (EnSite Array, St. Jude Medical) were collected and analyzed per patient. After QRST removal, the AEGs were divided into 4 s windows with a 50% overlap. Fast Fourier transform was used for DF identification. HDF areas were identified as the maximum DF to 0.25 Hz below that, and their centers of gravity (CGs) were used to track their spatiotemporal movement. Spectral organization measurements were estimated. Hilbert transform was used to calculate instantaneous phase. Results: The system was successfully used to guide catheter ablation for 10 persAF patients. The mean processing time was 10.4 ± 1.5 min, which is adequate comparing to the normal electrophysiological (EP) procedure time (120∼180 min). Conclusions: A customized software platform capable of measuring different forms of spatiotemporal AEG analysis was implemented and used in clinical environment to guide persAF ablation. The modular nature of the platform will help electrophysiological studies in understanding of the underlying AF mechanisms

Frequency Analysis of Atrial Fibrillation From the Surface Electrocardiogram

Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Neither the natural history of AF nor its response to therapy are sufficiently predictable by clinical and echocardiographic parameters. Atrial fibrillatory frequency (or rate) can reliably be assessed from the surface electrocardiogram (ECG) using digital signal processing (filtering, subtraction of averaged QRST complexes, and power spectral analysis) and shows large inter-individual variability. This measurement correlates well with intraatrial cycle length, a parameter which appears to have primary importance in AF domestication and response to therapy. AF with a low fibrillatory rate is more likely to terminate spontaneously, and responds better to antiarrhythmic drugs or cardioversion while high rate AF is more often persistent and refractory to therapy. In conclusion, frequency analysis of AF seems to be useful for non-invasive assessment of electrical remodeling in AF and may subsequently be helpful for guiding AF therapy

Mapping the Substrate of Atrial Fibrillation: Tools and Techniques

Atrial fibrillation (AF) is the most common cardiac arrhythmia that affects an estimated 33.5 million people worldwide. Despite its prevalence and economic burden, treatments remain relatively ineffective. Interventional treatments using catheter ablation have shown more success in cure rates than pharmacologic methods for AF. However, success rates diminish drastically in patients with more advanced forms of the disease. The focus of this research is to develop a mapping strategy to improve the success of ablation. To achieve this goal, I used a computational model of excitation in order to simulate atrial fibrillation and evaluate mapping strategies that could guide ablation. I first propose a substrate guided mapping strategy to allow patient-specific treatment rather than a one size fits all approach. Ablation guided by this method reduced AF episode durations compared to baseline durations and an equal amount of random ablation in computational simulations. Because the accuracy of electrogram mapping is dependent upon catheter-tissue contact, I then provide a method to identify the distance between the electrode recording sites and the tissue surface using only the electrogram signal. The algorithm was validated both in silico and in vivo. Finally, I develop a classification algorithm for the identification of activation patterns using simultaneous, multi-site electrode recordings to aid in the development of an appropriate ablation strategy during AF. These findings provide a framework for future mapping and ablation studies in humans and assist in the development of individualized ablation strategies for patients with higher disease burden

Effectiveness of atrial fibrillation rotor ablation is dependent on conduction velocity: An in-silico 3-dimensional modeling study.

BACKGROUND: We previously reported that stable rotors are observed in in-silico human atrial fibrillation (AF) models, and are well represented by a dominant frequency (DF). In the current study, we hypothesized that the outcome of DF ablation is affected by conduction velocity (CV) conditions and examined this hypothesis using in-silico 3D-AF modeling. METHODS: We integrated 3D CT images of left atrium obtained from 10 patients with persistent AF (80% male, 61.8±13.5 years old) into in-silico AF model. We compared AF maintenance durations (max 300s), spatiotemporal stabilities of DF, phase singularity (PS) number, life-span of PS, and AF termination or defragmentation rates after virtual DF ablation with 5 different CV conditions (0.2, 0.3, 0.4, 0.5, and 0.6m/s). RESULTS: 1. AF maintenance duration (p<0.001), spatiotemporal mean variance of DF (p<0.001), and the number of PS (p = 0.023) showed CV dependent bimodal patterns (highest at CV0.4m/s and lowest at CV0.6m/s) consistently. 2. After 10% highest DF ablation, AF defragmentation rates were the lowest at CV0.4m/s (37.8%), but highest at CV0.5 and 0.6m/s (all 100%, p<0.001). 3. In the episodes with AF termination or defragmentation followed by 10% highest DF ablation, baseline AF maintenance duration was shorter (p<0.001), spatiotemporal mean variance of DF was lower (p = 0.014), and the number of PS was lower (p = 0.004) than those with failed AF defragmentation after DF ablation. CONCLUSION: Virtual ablation of DF, which may indicate AF driver, was more likely to terminate or defragment AF with spatiotemporally stable DF, but not likely to do so in long-lasting and sustained AF conditions, depending on CV.ope