Non-invasive organization variation assessment in the onset and termination of paroxysmal atrial fibrillation

M/M/Infinity Birth-Death Processes - A Quantitative Representational Framework to Summarize and Explain Phase Singularity and Wavelet Dynamics in Atrial Fibrillation

Rationale

A quantitative framework to summarize and explain the quasi-stationary population dynamics of unstable phase singularities (PS) and wavelets in human atrial fibrillation (AF) is at present lacking. Building on recent evidence showing that the formation and destruction of PS and wavelets in AF can be represented as renewal processes, we sought to establish such a quantitative framework, which could also potentially provide insight into the mechanisms of spontaneous AF termination.

Objectives

Here, we hypothesized that the observed number of PS or wavelets in AF could be governed by a common set of renewal rate constants λ _f (for PS or wavelet formation) and λ _d (PS or wavelet destruction), with steady-state population dynamics modeled as an M/M/∞ birth-death process. We further hypothesized that changes to the M/M/∞ birth-death matrix would explain spontaneous AF termination.

Methods and Results

AF was studied in in a multimodality, multispecies study in humans, animal experimental models (rats and sheep) and Ramirez-Nattel-Courtemanche model computer simulations. We demonstrated: (i) that λ _f and λ _d can be combined in a Markov M/M/∞ process to accurately model the observed average number and population distribution of PS and wavelets in all systems at different scales of mapping; and (ii) that slowing of the rate constants λ _f and λ _d is associated with slower mixing rates of the M/M/∞ birth-death matrix, providing an explanation for spontaneous AF termination.

Conclusion

M/M/∞ birth-death processes provide an accurate quantitative representational architecture to characterize PS and wavelet population dynamics in AF, by providing governing equations to understand the regeneration of PS and wavelets during sustained AF, as well as providing insight into the mechanism of spontaneous AF termination.

Individualised Approaches for Catheter Ablation of AF: Patient Selection and Procedural Endpoints

Pulmonary vein isolation (PVI) is the cornerstone of AF ablation, but studies have reported improved efficacy with high rates of repeat procedures. Because of the large interindividual variability in the underlying electrical and anatomical substrate, achieving optimal outcomes requires an individualised approach. This includes optimal candidate selection as well as defined ablation strategies with objective procedure endpoints beyond PVI. Candidate selection is traditionally based on coarse and sometimes arbitrary clinical stratification such as AF type, but finer predictors of treatment efficacy including biomarkers, advanced imaging and electrocardiographic parameters have shown promise. Numerous ancillary ablation strategies beyond PVI have been investigated, but the absence of a clear mechanistic and evidence-based endpoint, unlike in other arrhythmias, has remained a universal limitation. Potential endpoints include functional ones such as AF termination or non-inducibility and substrate-based endpoints such as isolation of low-voltage areas. This review summarises the relevant literature and proposes guidance for clinical practice and future research.

Reference database and performance evaluation of methods for extraction of atrial fibrillatory waves in the ECG

OBJECTIVE

This study proposes a reference database, composed of a large number of simulated ECG signals in atrial fibrillation (AF), for investigating the performance of methods for extraction of atrial fibrillatory waves (f-waves).

APPROACH

The simulated signals are produced using a recently published and validated model of 12-lead ECGs in AF. The database is composed of eight signal sets together accounting for a wide range of characteristics known to represent major challenges in f-wave extraction, including high heart rates, high morphological QRST variability, and the presence of ventricular premature beats. Each set contains 30 5 min signals with different f-wave amplitudes. The database is used for the purpose of investigating the statistical association between different indices, designed for use with either real or simulated signals.

MAIN RESULTS

Using the database, available at the PhysioNet repository of physiological signals, the performance indices unnormalized ventricular residue (uVR), designed for real signals, and the root mean square error, designed for simulated signals, were found to exhibit the strongest association, leading to the recommendation that uVR should be used when characterizing performance in real signals.

SIGNIFICANCE

The proposed database facilitates comparison of the performance of different f-wave extraction methods and makes it possible to express performance in terms of the error between simulated and extracted f-wave signals.

Information Theory and Atrial Fibrillation (AF): A Review

Atrial Fibrillation (AF) is the most common cardiac rhythm disorder seen in hospitals and in general practice, accounting for up to a third of arrhythmia related hospitalizations. Unfortunately, AF treatment is in practice complicated by the lack of understanding of the fundamental mechanisms underlying the arrhythmia, which makes detection of effective ablation targets particularly difficult. Various approaches to AF mapping have been explored in the hopes of better pinpointing these effective targets, such as Dominant Frequency (DF) analysis, complex fractionated electrograms (CFAE) and unipolar reconstruction (FIRM), but many of these methods have produced conflicting results or require further investigation. Exploration of AF using information theoretic-based approaches may have the potential to provide new insights into the complex system dynamics of AF, whilst also providing the benefit of being less reliant on empirically derived definitions in comparison to alternate mapping approaches. This work provides an overview of information theory and reviews its applications in AF analysis, with particular focus on AF mapping. The works discussed in this review demonstrate how understanding AF from a signal property perspective can provide new insights into the arrhythmic phenomena, which may have valuable clinical implications for AF mapping and ablation in the future.

Sample Entropy Analysis of Noisy Atrial Electrograms during Atrial Fibrillation

Most cardiac arrhythmias can be classified as atrial flutter, focal atrial tachycardia, or atrial fibrillation. They have been usually treated using drugs, but catheter ablation has proven more effective. This is an invasive method devised to destroy the heart tissue that disturbs correct heart rhythm. In order to accurately localise the focus of this disturbance, the acquisition and processing of atrial electrograms form the usual mapping technique. They can be single potentials, double potentials, or complex fractionated atrial electrogram (CFAE) potentials, and last ones are the most effective targets for ablation. The electrophysiological substrate is then localised by a suitable signal processing method. Sample Entropy is a statistic scarcely applied to electrograms but can arguably become a powerful tool to analyse these time series, supported by its results in other similar biomedical applications. However, the lack of an analysis of its dependence on the perturbations usually found in electrogram data, such as missing samples or spikes, is even more marked. This paper applied SampEn to the segmentation between non-CFAE and CFAE records and assessed its class segmentation power loss at different levels of these perturbations. The results confirmed that SampEn was able to significantly distinguish between non-CFAE and CFAE records, even under very unfavourable conditions, such as 50% of missing data or 10% of spikes.

Electrocardiogram modeling during paroxysmal atrial fibrillation: application to the detection of brief episodes

OBJECTIVE

A model for simulating multi-lead ECG signals during paroxysmal atrial fibrillation (AF) is proposed.

SIGNIFICANCE

The model is of particular significance when evaluating detection performance in the presence of brief AF episodes, especially since annotated databases with such episodes are lacking.

APPROACH

The proposed model accounts for important characteristics such as switching between sinus rhythm and AF, varying P-wave morphology, repetition rate of f-waves, presence of atrial premature beats, and various types of noise.

MAIN RESULTS

Two expert cardiologists assessed the realism of simulated signals relative to real ECG signals, both in sinus rhythm and AF. The cardiologists identified the correct rhythm in all cases, and considered two-thirds of the simulated signals as realistic. The proposed model was also investigated by evaluating the performance of two AF detectors which explored either rhythm only or both rhythm and morphology. The results show that detection performance is strongly dependent on AF episode duration, and, consequently, demonstrate that the model can play a significant role in the investigation of detector properties.

Electrocardiographic Spectral Features for Long-Term Outcome Prognosis of Atrial Fibrillation Catheter Ablation

Atrial fibrillation (AF) is the most common arrhythmia in routine clinical practice. Despite many years of research, its mechanisms still are not well understood, thus reducing the effectiveness of AF treatments. Nowadays, pulmonary vein isolation by catheter ablation is the treatment of choice for AF resistant either to pharmacological or electrical cardioversion. However, given that long-term recurrences are common, an appropriate patient selection before the procedure is of paramount relevance in the improvement of AF catheter ablation outcome. The present work studies how several spectral features of the atrial activity (AA) from a single lead of the surface electrocardiogram (ECG) can become potential pre-ablation predictors of long-term (>2 months) sinus rhythm maintenance. Among all the analyzed spectral features, results indicated that the most significant single predictor of paroxysmal AF ablation treatment outcome was related to the amplitude of the first harmonic of the dominant frequency, providing sensitivity (Se), specificity (Sp) and accuracy (Ac) values of 90%, 42.86 and 77.78%, respectively. On the other hand, the AA harmonic structure was the most significant single predictor for persistent AF, with Se, Sp and Ac values of 100%, 54.55 and 77.27%, respectively. A logistic regression analysis, mainly based on spectral amplitudes as well as on the harmonic structure of the AA, provided a higher predictive ability both for paroxysmal AF (Se = 100%, Sp = 57.14% and Ac = 88.89%) and persistent AF (Se = 90.91%, Sp = 72.73 and Ac = 81.82%). In conclusion, the study of key AA spectral features from the surface ECG can provide a significant preoperative prognosis of AF catheter ablation outcome at long-term follow-up.

Alteration of the P-wave non-linear dynamics near the onset of paroxysmal atrial fibrillation

The analysis of P-wave variability from the electrocardiogram (ECG) has been suggested as an early predictor of the onset of paroxysmal atrial fibrillation (PAF). Hence, a preventive treatment could be used to avoid the loss of normal sinus rhythm, thus minimising health risks and improving the patient's quality of life. In these previous studies the variability of different temporal and morphological P-wave features has been only analysed in a linear fashion. However, the electrophysiological alteration occurring in the atria before the onset of PAF has to be considered as an inherently complex, chaotic and non-stationary process. This work analyses the presence of non-linear dynamics in the P-wave progression before the onset of PAF through the application of the central tendency measure (CTM), which is a non-linear metric summarising the degree of variability in a time series. Two hour-length ECG intervals just before the arrhythmia onset belonging to 46 different PAF patients were analysed. In agreement with the invasively observed inhomogeneous atrial conduction preceding the onset of PAF, CTM for all the considered P-wave features showed higher variability when the arrhythmia was closer to its onset. A diagnostic accuracy around 80% to discern between ECG segments far from PAF and close to PAF was obtained with the CTM of the metrics considered. This result was similar to previous P-wave variability methods based on linear approaches. However, the combination of linear and non-linear methods with a decision tree improved considerably their discriminant ability up to 90%, thus suggesting that both dynamics could coexist at the same time in the fragmented depolarisation of the atria preceding the arrhythmia.

Noninvasive time and frequency predictors of long-standing atrial fibrillation early recurrence after electrical cardioversion

BACKGROUND

Several clinical factors have been studied to predict atrial fibrillation (AF) recurrence after electrical cardioversion (ECV) with limited predictive value.

METHODS

 A method able to predict robustly long-standing AF early recurrence by characterizing noninvasively the electrical atrial activity (AA) with parameters related to its time course and spectral features is presented. To this respect, 63 patients (20 men and 43 women; mean age 73.4 ± 9.0 years; under antiarrhythmic drug treatment with amiodarone) who were referred for ECV of persistent AF were studied. During a 4-week follow-up, AF recurrence was observed in 41 patients (65.1%).

RESULTS

 RR variability and the studied AA spectral features, including dominant atrial frequency (DAF), its first harmonic and their amplitude, provided poor statistical differences between groups. On the contrary, f waves power (fWP) and Sample Entropy (SampEn) of the AA behaved as very good predictors. Patients who relapsed to AF presented lower fWP (0.036 ± 0.019 vs 0.081 ± 0.029 n.u.(2) , P < 0.001) and higher SampEn (0.107 ± 0.022 vs 0.086 ± 0.033, P < 0.01). Furthermore, fWP presented the highest predictive accuracy of 82.5%, whereas SampEn provided a 79.4%. The remaining features revealed accuracies lower than 70%. A stepwise discriminant analysis (SDA) provided a model based on fWP and SampEn with 90.5% of accuracy.

CONCLUSIONS

 The fWP has proved to predict long-standing AF early recurrence after ECV and can be combined with SampEn to improve its diagnostic ability. Furthermore, a thorough analysis of the results allowed outlining possible associations between these two features and the concomitant status of atrial remodeling.

Non-invasive atrial fibrillation organization follow-up under successive attempts of electrical cardioversion

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.

Surface ECG organization time course analysis along onward episodes of paroxysmal atrial fibrillation

The complete understanding of the mechanisms leading to the initiation, maintenance and self-termination of atrial fibrillation (AF) still is an unsolved challenge for cardiac electrophysiology. Studies in which AF has been induced have shown that electrophysiological and structural remodeling of the atria during the arrhythmia could play an important role in the transition from paroxysmal to persistent AF. However, to this day, the time course of the atrial remodeling along onward episodes of non-induced paroxysmal AF has not been investigated yet. In this work, a non-invasive method, based on the regularity estimation of AF through sample entropy (SampEn), has been used to assess the organization evolution along onward episodes of paroxysmal AF. Given that AF organization has been associated to the number of existing wavelets wandering throughout the atrial tissue, SampEn could be considered as a concomitant estimator of atrial remodeling. The achieved results, in close agreement with previous findings obtained from invasive recordings, proved several relevant aspects of arial remodeling. Firstly, a progressive disorganization increase (SampEn increase) along onward episodes of AF has been observed for 63% of the analyzed patients, whereas a stable AF organization degree has been appreciated in the remaining 37%. Next, a positive correlation between episode duration and SampEn has been obtained (R=0.541, p<0.01). Finally, a remarkable influence of the fibrillation-free interval, preceding each episode, on the corresponding level of AF organization at the onset of the subsequent AF episode has been observed, with a correlation between these two indices of R=0.389 (p<0.01). As a consequence, it could be considered that atrial electrophysiological dynamics that occur along onward paroxysmal AF episodes are reflected and can be quantified from ECG recordings through non-invasive organization estimation.

Noninvasive organization analysis along consecutive episodes of paroxysmal atrial fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia in clinical practice. Although its mechanisms are incompletely understood, electrophysiological and structural remodeling of the atria seem to play an important role in the arrhythmia transition from paroxysmal to persistent. However, the time course of the atrial remodeling along onward episodes of non-induced paroxysmal AF has not been investigated yet. In this work, a non-invasive method, based on the regularity estimation of AF through sample entropy (SampEn), has been used to assess the organization evolution along onward episodes of paroxysmal AF. Given that AF organization has been associated to the number of existing wavelets wandering throughout the atrial tissue, SampEn could be considered as a concomitant estimator of atrial remodeling. The achieved results, in close agreement with previous findings obtained from invasive recordings, showed a progressive disorganization increase along onward episodes of AF for 63% of the analyzed patients and a stable AF organization degree in the remaining 37%. Additionally, a positive correlation between episode duration and SampEn was also noticed (R = 0.541, p < 0.01). As a consequence, it could be considered that atrial electrophysiological dynamics that occur along onward paroxysmal AF episodes are reflected and can be quantified from ECG recordings through non-invasive organization estimation.

Characterizing atypical patterns of Heart Rate before Paroxysmal Ventricular Tachycardia

Results of a comparative analysis between Heart Rate (HR) patterns occurring before the onset of Paroxysmal Ventricular Tachycardia (PVT) and from healthy subjects are shown. Two study groups were made after electrocardiographic dynamical monitoring (Holter) of volunteers. The first group includes 100h from 27 healthy control subjects, and the second group consists of 88h, ending with self-terminating episodes of PVT from 55 patients. Patterns are defined as sequences of consecutive RR intervals, while atypical patterns are defined as corresponding to unlikely behavior of Heart Rate in healthy subjects. We investigated spatial and temporal distributions of these patterns in order to find early signs of PVT. We found that they can be grouped on a reduced number of clusters, and the number of atypical patterns increases as we approach the onset of the episode.

Optimal parameters study for sample entropy-based atrial fibrillation organization analysis

Sample entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended and widely used in the literature for the application of SampEn to some physiological time series, such as heart rate, hormonal data, etc. However, no guidelines exist for the selection of that values in other cases. Therefore, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In the present work, a thorough analysis on the optimal values for m, r and N is presented within the context of atrial fibrillation (AF) organization estimation, computed from surface electrocardiogram recordings. Recently, the evaluation of AF organization through SampEn, has revealed clinically useful information that could be used for a better treatment of this arrhythmia. The present study analyzed optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF. As a result, interesting recommendations about the selection of m, r and N, together with the relationship between N and the sampling rate (f(s)) were obtained. More precisely, (i) the proportion between N and f(s) should be higher than 1s and f(s)>or=256 Hz, (ii) overlapping between adjacent N-length windows does not improve AF organization estimation with respect to the analysis of non-overlapping windows, and (iii) values of m and r maximizing successful classification for the analyzed AF databases should be considered within a range wider than the proposed in the literature for heart rate analysis, i.e. m=1 and m=2 and r between 0.1 and 0.25 times the standard deviation of the data.

Noninvasive assessment of the complexity and stationarity of the atrial wavefront patterns during atrial fibrillation

A novel automated approach to quantitatively evaluate the degree of spatio-temporal organization in the atrial activity (AA) during atrial fibrillation (AF) from surface recordings, obtained from body surface potential maps (BSPM), is presented. AA organization is assessed by measuring the reflection of the spatial complexity and temporal stationarity of the wavefront patterns propagating inside the atria on the surface ECG, by means of principal component analysis (PCA). Complexity and stationarity are quantified through novel parameters describing the structure of the mixing matrices derived by the PCA of the different AA segments across the BSPM recording. A significant inverse correlation between complexity and stationarity is highlighted by this analysis. The discriminatory power of the parameters in identifying different groups in the set of patients under study is also analyzed. The obtained results present analogies with earlier invasive studies in terms of number of significant components necessary to describe 95% of the variance in the AA (four for more organized AF, and eight for more disorganized AF). These findings suggest that automated analysis of AF organization exploiting spatial diversity in surface recordings is indeed possible, potentially leading to an improvement in clinical decision making and AF treatment.