Spatial variability of the 12-lead surface ECG as a tool for noninvasive prediction of catheter ablation outcome in persistent atrial fibrillation

Combination of frequency- and time-domain characteristics of the fibrillatory waves for enhanced prediction of persistent atrial fibrillation recurrence after catheter ablation

Catheter ablation (CA) remains the cornerstone alternative to cardioversion for sinus rhythm (SR) restoration in patients with atrial fibrillation (AF). Unfortunately, despite the last methodological and technological advances, this procedure is not consistently effective in treating persistent AF. Beyond introducing new indices to characterize the fibrillatory waves (f-waves) recorded through the preoperative electrocardiogram (ECG), the aim of this study is to combine frequency- and time-domain features to improve CA outcome prediction and optimize patient selection for the procedure, given the absence of any study that jointly analyzes information from both domains. Precisely, the f-waves of 151 persistent AF patients undergoing their first CA procedure were extracted from standard V1 lead. Novel spectral and amplitude features were derived from these waves and combined through a machine learning algorithm to anticipate the intervention mid-term outcome. The power rate index (φ), which estimates the power of the harmonic content regarding the dominant frequency (DF), yielded the maximum individual discriminant ability of 64% to discern between individuals who experienced a recurrence of AF and those who sustained SR after a 9-month follow-up period. The predictive accuracy was improved up to 78.5% when this parameter φ was merged with the amplitude spectrum area in the DF bandwidth (A M S A L F ) and the normalized amplitude of the f-waves into a prediction model based on an ensemble classifier, built by random undersampling boosting of decision trees. This outcome suggests that the synthesis of both spectral and temporal features of the f-waves before CA might enrich the prognostic knowledge of this therapy for persistent AF patients.

The frequency of atrial fibrillatory waves is modulated by the spatiotemporal pattern of acetylcholine release: a 3D computational study

In atrial fibrillation (AF), the ECG P-wave, which represents atrial depolarization, is replaced with chaotic and irregular fibrillation waves (f waves). The f-wave frequency, F f, shows significant variations over time. Cardiorespiratory interactions regulated by the autonomic nervous system have been suggested to play a role in such variations. We conducted a simulation study to test whether the spatiotemporal release pattern of the parasympathetic neurotransmitter acetylcholine (ACh) modulates the frequency of atrial reentrant circuits. Understanding parasympathetic involvement in AF may guide more effective treatment approaches and could help to design autonomic markers alternative to heart rate variability (HRV), which is not available in AF patients. 2D tissue and 3D whole-atria models of human atrial electrophysiology in persistent AF were built. Different ACh release percentages (8% and 30%) and spatial ACh release patterns, including spatially random release and release from ganglionated plexi (GPs) and associated nerves, were considered. The temporal pattern of ACh release, ACh(t), was simulated following a sinusoidal waveform of frequency 0.125 Hz to represent the respiratory frequency. Different mean concentrations ( A C h ¯ ) and peak-to-peak ranges of ACh (ΔACh) were tested. We found that temporal variations in F f, F f(t), followed the simulated temporal ACh(t) pattern in all cases. The temporal mean of F f(t), F ¯ f , depended on the fibrillatory pattern (number and location of rotors), the percentage of ACh release nodes and A C h ¯ . The magnitude of F f(t) modulation, ΔF f, depended on the percentage of ACh release nodes and ΔACh. The spatial pattern of ACh release did not have an impact on F ¯ f and only a mild impact on ΔF f. The f-wave frequency, being indicative of vagal activity, has the potential to drive autonomic-based therapeutic actions and could replace HRV markers not quantifiable from AF patients.

A 3D-CNN with temporal-attention block to predict the recurrence of atrial fibrillation based on body-surface potential mapping signals

Catheter ablation has become an important treatment for atrial fibrillation (AF), but its recurrence rate is still high. The aim of this study was to predict AF recurrence using a three-dimensional (3D) network model based on body-surface potential mapping signals (BSPMs). BSPMs were recorded with a 128-lead vest in 14 persistent AF patients before undergoing catheter ablation (Maze-IV). The torso geometry was acquired and meshed by point cloud technology, and the BSPM was interpolated into the torso geometry by the inverse distance weighted (IDW) method to generate the isopotential map. Experiments show that the isopotential map of BSPMs can reflect the propagation of the electrical wavefronts. The 3D isopotential sequence map was established by combining the spatial–temporal information of the isopotential map; a 3D convolutional neural network (3D-CNN) model with temporal attention was established to predict AF recurrence. Our study proposes a novel attention block that focuses the characteristics of atrial activations to improve sampling accuracy. In our experiment, accuracy (ACC) in the intra-patient evaluation for predicting the recurrence of AF was 99.38%. In the inter-patient evaluation, ACC of 3D-CNN was 81.48%, and the area under the curve (AUC) was 0.88. It can be concluded that the dynamic rendering of multiple isopotential maps can not only comprehensively display the conduction of cardiac electrical activity on the body surface but also successfully predict the recurrence of AF after CA by using 3D isopotential sequence maps.

A comparative study of the performance of methods for f-wave extraction

Objective.This study proposes a novel technique for atrial fibrillatory waves (f-waves) extraction and investigates the performance of the proposed method comparing with different f-wave extraction methods.Approach.We propose a novel technique combining a periodic component analysis (PiCA) and echo state network (ESN) for f-waves extraction, denoted PiCA-ESN. PiCA-ESN benefits from the advantages of using both source separation and nonlinear adaptive filtering. PiCA-ESN is evaluated by comparing with other state-of-the-art approaches, which include template subtraction technique based on principal component analysis, spatiotemporal cancellation, nonlinear adaptive filtering using an echo state neural network, and a source separation technique based on PiCA. Quality assessment is performed on a recently published reference database including a large number of simulated ECG signals in atrial fibrillation (AF). The performance of the f-wave extraction methods is evaluated in terms of signal quality metrics (SNR, ΔSNR) and robustness of f-wave features.Main results.The proposed method offers the best signal quality performance, with a ΔSNR of approximately 22 dB across all 8 sets of the reference database, as well as the most robust extraction of f-wave features, with 75% of all estimates of dominant atrial frequency well below 1 Hz.

Surface ECG-based complexity parameters for predicting outcomes of catheter ablation for nonparoxysmal atrial fibrillation: efficacy of fibrillatory wave amplitude

Catheter ablation (CA) is a well-established therapy for rhythm control in atrial fibrillation (AF). However, CA outcomes for persistent AF remain unsatisfactory because of the high recurrence rate despite time-consuming efforts and the latest ablation technology. Therefore, the selection of good responders to CA is necessary. Surface electrocardiography (sECG)-based complexity parameters were tested for the predictive ability of procedural termination failure during CA and late recurrence of atrial arrhythmias (AA) after CA. A total of 130 patients with nonparoxysmal AF who underwent CA for the first time were investigated. A 10-second sECG of 4 leads (leads I, II, V1, and V6) was analyzed to compute the fibrillatory wave amplitude (FWA), dominant frequency (DF), spectral entropy (SE), organization index (OI), and sample entropy (SampEn). The study endpoints were procedural termination failure during CA and late (≥1 year) AA recurrence after CA. In the multivariate analysis, FWA in lead V1 and DF in lead I were independent predictors of successful AF termination during CA (P <.05). The optimal cut-off values for FWA in lead V1 and DF in lead I were 60.38 μV (area under the curve [AUC], 0.672; P = .001) and 5.7 Hz (AUC, 0.630; P = .016), respectively. The combination of FWA of lead V1 and DF of lead I had a more powerful odds ratio for predicting procedural termination failure (OR, 8.542; 95% CI, 2.938-28.834; P < .001). FWA in lead V1 was the only independent predictor of late recurrence after CA. The cut-off value is 65.73 μV which was 0.634 of the AUC (P = .009). These sECG parameters, FWA in lead V1 and DF in lead I, predicted AF termination by CA in patients with nonparoxysmal AF. In particular, FWA in lead V1 was an independent predictor of late recurrence of AA after CA.

Fibrillatory Wave Amplitude Evolution during Persistent Atrial Fibrillation Ablation: Implications for Atrial Substrate and Fibrillation Complexity Assessment

Background. Fibrillatory Wave Amplitude (FWA) has been described as a non-invasive marker of atrial fibrillation (AF) complexity, and it predicts catheter ablation outcome. However, the actual determinants of FWA remain incompletely understood. Objective. To assess the respective implications of anatomical atrial substrate and AF spectral characteristics for FWA. Methods. Persistent AF patients undergoing radiofrequency catheter ablation were included. FWA was measured on 1-min ECG by TQ concatenation in Lead I, V1, V2, and V5 at baseline and immediately before AF termination. FWA evolution during ablation was compared to that of AF dominant frequency (DF) measured by Independent Component Analysis on 12-lead ECG. FWA was compared to the extent of endocardial low-voltage areas (LVA I < 10%; II 10–20%; III 20–30%; IV > 30%), to the surface of healthy left atrial tissue, and to P-wave amplitude in sinus rhythm. The predictive value of FWA for AF recurrence during follow-up was assessed. Results. We included 29 patients. FWA remained stable along ablation procedure with comparable values at baseline and before AF termination (Lead I p = 0.54; V1 p = 0.858; V2 p = 0.215; V5 p = 0.14), whereas DF significantly decreased (5.67 ± 0.68 vs. 4.95 ± 0.58 Hz, p < 0.001). FWA was higher in LVA-I than in LVA-II, -III, and -IV in Lead I and V5 (p = 0.02 and p = 0.01). FWA in V5 was strongly correlated with the surface of healthy left atrial tissue (R = 0.786; p < 0.001). FWA showed moderate to strong correlation to P-wave amplitude in all leads. Finally, FWA did not predict AF recurrence after a follow-up of 23.3 ± 9.8 months. Conclusions. These findings suggest that FWA is unrelated to AF complexity but is mainly determined by the amount of viable atrial myocytes. Therefore, FWA should only be referred as a marker of atrial tissue pathology.

Extended ECG Improves Classification of Paroxysmal and Persistent Atrial Fibrillation Based on P- and f-Waves

Background

The standard 12-lead ECG has been shown to be of value in characterizing atrial conduction properties. The added value of extended ECG recordings (longer recordings from more sites) has not been systematically explored yet.

Objective

The aim of this study is to employ an extended ECG to identify characteristics of atrial electrical activity related to paroxysmal vs. persistent atrial fibrillation (AF).

Methods

In 247 participants scheduled for AF ablation, an extended ECG was recorded (12 standard plus 3 additional leads, 5 min recording, no filtering). For patients presenting in sinus rhythm (SR), the signal-averaged P-wave and the spatiotemporal P-wave variability was analyzed. For patients presenting in AF, f-wave properties in the QRST (the amplitude complex of the ventricular electrical activity: Q-, R-, S-, and T-wave)-canceled ECG were determined.

Results

Significant differences between paroxysmal (N = 152) and persistent patients with AF (N = 95) were found in several P-wave and f-wave parameters, including parameters that can only be calculated from an extended ECG. Furthermore, a moderate, but significant correlation was found between echocardiographic parameters and P-wave and f-wave parameters. There was a moderate correlation of left atrial (LA) diameter with P-wave energy duration (r = 0.317, p < 0.001) and f-wave amplitude in lead A3 (r = -0.389, p = 0.002). The AF-type classification performance significantly improved when parameters calculated from the extended ECG were taken into account [area under the curve (AUC) = 0.58, interquartile range (IQR) 0.50-0.64 for standard ECG parameters only vs. AUC = 0.76, IQR 0.70-0.80 for extended ECG parameters, p < 0.001].

Conclusion

The P- and f-wave analysis of extended ECG configurations identified specific ECG features allowing improved classification of paroxysmal vs. persistent AF. The extended ECG significantly improved AF-type classification in our analyzed data as compared to a standard 10-s 12-lead ECG. Whether this can result in a better clinical AF type classification warrants further prospective study.

Classification of Fibrillation Organisation Using Electrocardiograms to Guide Mechanism-Directed Treatments

Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustained. Determining the underlying mechanisms of fibrillation may be helpful in tailoring treatment strategies. We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning. Methods: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. BP features were derived from surface ECGs and fed to a linear discriminant analysis classifier to predict fibrillation organisation level. Two datasets, single-channel ECGs of rat VF (n = 9) and 12-lead ECGs of human AF (n = 17), were used for model evaluation in a leave-one-out (LOO) manner. Results: The proposed method correctly predicted the organisation level from rat VF ECG with the sensitivity of 75%, specificity of 80%, and accuracy of 78%, and from clinical AF ECG with the sensitivity of 80%, specificity of 92%, and accuracy of 88%. Conclusion: Our proposed method can distinguish between AF/VF of different global organisation levels non-invasively from the ECG alone. This may aid in patient selection and guiding mechanism-directed tailored treatment strategies.

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals-Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study

Background

When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored.

Objective

We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead–based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power.

Methods

We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked.

Results

The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads—ideally more than 4 leads—is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction.

Conclusions

By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders.

Respiratory Induced Modulation in f-Wave Characteristics During Atrial Fibrillation

The autonomic nervous system (ANS) is an important factor in cardiac arrhythmia, and information about ANS activity during atrial fibrillation (AF) may contribute to personalized treatment. In this study we aim to quantify respiratory modulation in the f-wave frequency trend from resting ECG. First, an f-wave signal is extracted from the ECG by QRST cancelation. Second, an f-wave model is fitted to the f-wave signal to obtain a high resolution f-wave frequency trend and an index for signal quality control ( S ). Third, respiratory modulation in the f-wave frequency trend is extracted by applying a narrow band-pass filter. The center frequency of the band-pass filter is determined by the respiration rate. Respiration rate is estimated from a surrogate respiration signal, obtained from the ECG using homomorphic filtering. Peak conditioned spectral averaging, where spectra of sufficient quality from different leads are averaged, is employed to obtain a robust estimate of the respiration rate. The envelope of the filtered f-wave frequency trend is used to quantify the magnitude of respiratory induced f-wave frequency modulation. The proposed methodology is evaluated using simulated f-wave signals obtained using a sinusoidal harmonic model. Results from simulated signals show that the magnitude of the respiratory modulation is accurately estimated, quantified by an error below 0.01 Hz, if the signal quality is sufficient ( S > 0 . 5 ). The proposed method was applied to analyze ECG data from eight pacemaker patients with permanent AF recorded at baseline, during controlled respiration, and during controlled respiration after injection of atropine, respectively. The magnitude of the respiratory induce f-wave frequency modulation was 0.15 ± 0.01, 0.18 ± 0.02, and 0.17 ± 0.03 Hz during baseline, controlled respiration, and post-atropine, respectively. Our results suggest that parasympathetic regulation affects the magnitude of respiratory induced f-wave frequency modulation.

ECG-Based Indices to Characterize Persistent Atrial Fibrillation Before and During Stepwise Catheter Ablation

Background: Consistently successful patient outcomes following catheter ablation (CA) for treatment of persistent atrial fibrillation (pers-AF) remain elusive. We propose an electrocardiogram (ECG) analysis designed to (1) refine selection of patients most likely to benefit from ablation, and (2) examine the temporal evolution of AF organization indices that could act as clinical indicators of ongoing ablation effectiveness and completeness.

Method: Twelve-lead ECG was continuously recorded in 40 patients (61 ± 8 years) during stepwise CA (step-CA) procedures for treatment of pers-AF (sustained duration 19 ± 11 months). Following standard pre-processing, ECG signals were divided into 10-s epochs and labeled according to their temporal placement: pre-PVI (baseline), dur-PVI (during pulmonary vein isolation), and post-PVI (during complex-fractionated atrial electrograms and linear ablation). Instantaneous frequency (IF), adaptive organization index (AOI), sample entropy (SampEn) and f-wave amplitude (FWA) measures were calculated and analyzed during each of the three temporal steps. Temporal evolution of these measures was assessed using a statistical test for mean value transitions, as an indicator of changes in AF organization. Results were then compared between: (i) patients grouped according to step-CA outcome; (ii) patients grouped according to type of arrhythmia recurrence following the procedure, if applicable; (iii) within the same patient group during the three different temporal steps.

Results: Stepwise CA patient outcomes were as follows: (1) left-atrium (LA) terminated, not recurring (LTN, n = 8), (2) LA terminated, recurring (LTR, n = 20), and (3) not LA terminated, all recurring at follow-up (NLT, n = 12). Among the LTR and NLT patients, recurrence occurred as AF in seven patients and atrial tachycardia or atrial flutter (AT/AFL) in the remaining 25 patients. The ECG measures indicated the lowest level of organization in the NLT group for all ablation steps. The highest organization was observed in the LTN group, while the LTR group displayed an intermediate level of organization. Regarding time evolution of ECG measures in dur-PVI and post-PVI recordings, stepwise ablation led to increases in AF organization in most patients, with no significant differences between the LTN, LTR, and NLT groups. The median decrease in IF and increase in AOI were significantly greater in AT/AFL recurring patients than in AF recurring patients; however, changes in the SampEn and FWA parameters were not significantly different between types of recurrence.

Conclusion: Noninvasive ECG measures, though unable to predict arrhythmia recurrence following ablation, show the lowest levels of AF organization in patients that do not respond well to step-CA. Increasing AF organization in post-PVI may be associated with organized arrhythmia recurrence after a single ablation procedure.

Insights Into the Spatiotemporal Patterns of Complexity of Ventricular Fibrillation by Multilead Analysis of Body Surface Potential Maps

Background

Ventricular fibrillation (VF) is the main cause of sudden cardiac death, but its mechanisms are still unclear. We propose a noninvasive approach to describe the progression of VF complexity from body surface potential maps (BSPMs).

Methods

We mapped 252 VF episodes (16 ± 10 s) with a 252-electrode vest in 110 patients (89 male, 47 ± 18 years): 50 terminated spontaneously, otherwise by electrical cardioversion (DCC). Changes in complexity were assessed between the onset (“VF start”) and the end (“VF end”) of VF by the nondipolar component index (NDI_BSPM), measuring the fraction of energy nonpreserved by an equivalent 3D dipole from BSPMs. Higher NDI reflected lower VF organization. We also examined other standard body surface markers of VF dynamics, including fibrillatory wave amplitude (A_BSPM), surface cycle length (BsCL_BSPM) and Shannon entropy (ShEn_BSPM). Differences between patients with and without structural heart diseases (SHD, 32 vs. NSHD, 78) were also tested at those stages. Electrocardiographic features were validated with simultaneous endocardium cycle length (CL) in a subset of 30 patients.

Results

All BSPM markers measure an increase in electrical complexity during VF (p < 0.0001), and more significantly in NSHD patients. Complexity is significantly higher at the end of sustained VF episodes requiring DCC. Intraepisode intracardiac CL shortening (VF start 197 ± 24 vs. VF end 169 ± 20 ms; p < 0.0001) correlates with an increase in NDI, and decline in surface CL, f-wave amplitude, and entropy (p < 0.0001). In SHD patients VF is initially more complex than in NSHD patients (NDI_BSPM, p = 0.0007; ShEn_BSPM, p < 0.0001), with moderately slower (BsCL_BSPM, p = 0.06), low-amplitude f-waves (A_BSPM, p < 0.0001). In this population, lower NDI (p = 0.004) and slower surface CL (p = 0.008) at early stage of VF predict self-termination. In the NSHD group, a more abrupt increase in VF complexity is quantified by all BSPM parameters during sustained VF (p < 0.0001), whereas arrhythmia evolution is stable during self-terminating episodes, hinting at additional mechanisms driving VF dynamics.

Conclusion

Multilead BSPM analysis underlines distinct degrees of VF complexity based on substrate characteristics.

A novel framework for noninvasive analysis of short-term atrial activity dynamics during persistent atrial fibrillation

ECG-based representation of atrial fibrillation (AF) progression is currently limited. We propose a novel framework for a more sensitive noninvasive characterization of the AF substrate during persistent AF. An atrial activity (AA) recurrence signal is computed from body surface potential map (BSPM) recordings, and a set of characteristic indices is derived from it which captures the short- and long-term recurrent behaviour in the AA patterns. A novel measure of short- and long-term spatial variability of AA propagation is introduced, to provide an interpretation of the above indices, and to test the hypothesis that the variability in the oscillatory content of AA is due mainly to a spatially uncoordinated propagation of the AF waveforms. A simple model of atrial signal dynamics is proposed to confirm this hypothesis, and to investigate a possible influence of the AF substrate on the short-term recurrent behaviour of AA propagation. Results confirm the hypothesis, with the model also revealing the above influence. Once the characteristic indices are normalized to remove this influence, they show to be significantly associated with AF recurrence 4 to 6 weeks after electrical cardioversion. Therefore, the proposed framework improves noninvasive AF substrate characterization in patients with a very similar substrate. Graphical Abstract Schematic representation of the proposed framework for the noninvasive characterization of short-term atrial signal dynamics during persistent AF. The proposed framework shows that the faster the AA is propagating, the more stable its propagation paths are in the short-term (larger values of Speed in the bottom right plot should be interpreted as lower speed of propagation of the corresponding AA propagation patters).

ECG-Derived Respiratory Rate in Atrial Fibrillation

OBJECTIVE

The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated.

METHODS

The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals.

RESULTS

Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015 ± 0.021 Hz and 0.019 ± 0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034 ± 0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA.

CONCLUSION

The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed.

SIGNIFICANCE

The respiratory rate can be robustly estimated from the ECG in the presence of AF.

Changes in f-wave characteristics during cryoballoon catheter ablation

OBJECTIVE

Changes in ECG-derived parameters are studied in atrial fibrillation (AF) patients undergoing cryoballoon catheter ablation.

APPROACH

Parameters characterizing f-wave frequency, morphology by phase dispersion, and amplitude are estimated using a model-based statistical approach. These parameters are studied before, during, and after ablation, as well as for AF type (paroxysmal/persistent). Seventy-seven (49/28 paroxysmal/persistent) AF patients undergoing de novo catheter ablation are included in the study, out of which 31 (16/15 paroxysmal/persistent) were in AF during the whole procedure. A signal quality index (SQI) is used to identify analyzable segments.

MAIN RESULTS

f-wave frequency decreased significantly during ablation (p  =  0.001), in particular after ablation of the inferior right pulmonary vein (p  <  0.05). Frequency and phase dispersion differed significantly between paroxysmal and persistent AF (p  =  0.001 and p  <  0.05, respectively).

SIGNIFICANCE

This study demonstrates that a decrease in f-wave frequency can be distinguished during catheter ablation. The use of an SQI ensures reliable analysis and produces results significantly different from those obtained without an SQI.

Noninvasive Assessment of Atrial Fibrillation Complexity in Relation to Ablation Characteristics and Outcome

Background: The use of surface recordings to assess atrial fibrillation (AF) complexity is still limited in clinical practice. We propose a noninvasive tool to quantify AF complexity from body surface potential maps (BSPMs) that could be used to choose patients who are eligible for AF ablation and assess therapy impact.

Methods: BSPMs (mean duration: 7 ± 4 s) were recorded with a 252-lead vest in 97 persistent AF patients (80 male, 64 ± 11 years, duration 9.6 ± 10.4 months) before undergoing catheter ablation. Baseline cycle length (CL) was measured in the left atrial appendage. The procedural endpoint was AF termination. The ablation strategy impact was defined in terms of number of regions ablated, radiofrequency delivery time to achieve AF termination, and acute outcome. The atrial fibrillatory wave signal extracted from BSPMs was divided in 0.5-s consecutive segments, each projected on a 3D subspace determined through principal component analysis (PCA) in the current frame. We introduced the nondipolar component index (NDI) that quantifies the fraction of energy retained after subtracting an equivalent PCA dipolar approximation of heart electrical activity. AF complexity was assessed by the NDI averaged over the entire recording and compared to ablation strategy.

Results: AF terminated in 77 patients (79%), whose baseline AF CL was 177 ± 40 ms, whereas it was 157 ± 26 ms in patients with unsuccessful ablation outcome (p = 0.0586). Mean radiofrequency emission duration was 35 ± 21 min; 4 ± 2 regions were targeted. Long-lasting AF patients (≥12 months) exhibited higher complexity, with higher NDI values (≥12 months: 0.12 ± 0.04 vs. <12 months: 0.09 ± 0.03, p < 0.01) and short CLs (<160 ms: 0.12 ± 0.03 vs. between 160 and 180 ms: 0.10 ± 0.03 vs. >180 ms: 0.09 ± 0.03, p < 0.01). More organized AF as measured by lower NDI was associated with successful ablation outcome (termination: 0.10 ± 0.03 vs. no termination: 0.12 ± 0.04, p < 0.01), shorter procedures (<30 min: 0.09 ± 0.04 vs. ≥30 min: 0.11 ± 0.03, p < 0.001) and fewer ablation targets (<4: 0.09 ± 0.03 vs. ≥4: 0.11 ± 0.04, p < 0.01).

Conclusions: AF complexity can be noninvasively quantified by PCA in BSPMs and correlates with ablation outcome and AF pathophysiology.

Noninvasive mapping reveals recurrent and suddenly changing patterns in atrial fibrillation-a magnetocardiographic study

OBJECTIVE

To study noninvasive magnetocardiographic (MCG) mapping of ongoing atrial fibrillation (AF) and, for the possible mapping patterns observed, to develop simplified but meaningful descriptors or parameters, providing a possible basis for future research and clinical use of the mappings.

APPROACH

MCG mapping with simultaneous ECG was recorded during arrhythmia in patients representing a range of typical, clinically classical atrial arrhythmias. The recordings were assessed using MCG map animations, and a method to compute magnetic field map orientation (MFO) and its time course was created to facilitate presentation of the findings. All the data were segmented into four categories of ECG waveform regularity.

MAIN RESULTS

In visual observation of the MCG animations, an abundance of clear spatial and temporal patterns with regularity were found, often perceived as rotations of the map. This rotation and its sudden reversals of direction were distinctly present in the time course of the MFO. The shortest segments with consistent rotation lasted for some hundreds of milliseconds, i.e. a couple of cycles, but segments lasting for tens of seconds were observed as well. In the ECG, all four categories of regularity were present. The rotation of the MFO was observed in all patients under study and regardless of the ECG categories. Further, a change in ECG category during a measurement was frequently, but not always, found to be simultaneous with a change in the rotation pattern of the MFO. Utilization of spatial information of MCG mapping could enable detection of both regularities and instantaneous phenomena during AF.

SIGNIFICANCE

Cardiac mapping may offer a useful noninvasive means to study the mechanisms of AF, including superior temporal resolution.

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.

Empirical mode decomposition of multiple ECG leads for catheter ablation long-term outcome prediction in persistent atrial fibrillation

Predictive models arouse increasing interest in clinical practice, not only to improve successful intervention rates but also to extract information of diverse physiological disorders. This is the case of persistent atrial fibrillation (AF), the most common cardiac arrhythmia in adults. Currently, catheter ablation (CA) is one of the preferred therapies to face this disease. However, selecting the best responders to CA by standard noninvasive techniques such as the electrocardiogram (ECG) remains a challenge. This work presents different predictive models for determining long-term CA outcome based on the dominant frequency (DF) of atrial activity measured in the ECG. The ensemble empirical mode decomposition (EEMD) is employed to obtain the intrinsic mode functions (IMFs) composing the ECG signal in each lead. The IMF DFs computed in multiple leads are then combined into a logistic regression (LR) model. The IMF DF features are discriminant enough to reach 79% accuracy for long-term CA outcome prediction, outperforming other methods based on DF computation. Our study shows EEMD as a valuable alternative to extract clinically relevant spectral information from AF ECGs and confirms the advantage of LR to build multivariate predictive models as compared with univariate analysis.

Study of atrial activities for abnormality detection by phase rectified signal averaging technique

Non-invasive detection of Atrial Fibrillation (AF) and Atrial Flutter (AFL) from ECG at the time of their onset can prevent forthcoming dangers for patients. In most of the previous detection algorithms, one of the steps includes filtering of the signal to remove noise and artefacts present in the signal. In this paper, a method of AF and AFL detection is proposed from ECG without the conventional filtering stage. Here Phase Rectified Signal Average (PRSA) technique is used with a novel optimized windowing method to achieve an averaged signal without quasi-periodicities. Both time domain and statistical features are extracted from a novel SQ concatenated section of the signal for non-linear Support Vector Machine (SVM) based classification. The performance of the proposed algorithm is tested with the MIT-BIH Arrhythmia database and good performance parameters are obtained, as indicated in the result section.

Preoperative study of the surface ECG for the prognosis of atrial fibrillation maze surgery outcome at discharge

The Cox-maze surgery is an effective procedure for terminating atrial fibrillation (AF) in patients requiring open-heart surgery associated with another heart disease. After the intervention, regardless of the patient's rhythm, all are treated with oral anticoagulants and antiarrhythmic drugs prior to discharge. Furthermore, patients maintaining AF before discharge could also be treated with electrical cardioversion (ECV). In view of this, a preoperative prognosis of the patient's rhythm at discharge would be helpful for optimizing drug therapy planning as well as for advancing ECV therapy. This work analyzes 30 preoperative electrocardiograms (ECGs) from patients suffering from AF in order to predict the Cox-maze surgery outcome at discharge. Two different characteristics of the AF pattern have been studied. On the one hand, the atrial activity (AA) organization, which provides information about the number of propagating wavelets in the atria, was investigated. AA organization has been successfully used in previous studies related to spontaneous reversion of paroxysmal AF and to the outcome of ECV. To assess organization, the dominant atrial frequency (DAF) and sample entropy (SampEn) have been computed. On the other hand, the second characteristic studied was the fibrillatory wave (f-wave) amplitude, which has been demonstrated to be a valuable indicator of the Cox-maze surgery outcome in previous studies. Moreover, this parameter has been obtained through a new methodology, based on computing the f-wave average power (fWP). Finally, all the computed indices were combined in a decision tree in order to improve prediction capability. Results for the DAF yielded a sensitivity (Se), a specificity (Sp) and an accuracy (Acc) of 61.54%, 82.35% and 73.33%, respectively. For SampEn the values were 69.23%, 76.00% and 73.33%, respectively, and for fWP they were 92.31%, 82.35% and 86.67%, respectively. Finally, the decision tree combining the three parameters analyzed improved the preoperative prognosis of the Cox-maze outcome with values of Se, Sp and Acc of 100%, 82.35% and 90%, respectively. As a consequence, the analysis of parameters related to the f-wave pattern, extracted from the preoperative ECG, has provided a considerable ability to predict the outcome of AF Cox-maze surgery at discharge.

Noninvasive prediction of catheter ablation acute outcome in persistent atrial fibrillation based on logistic regression of ECG fibrillatory wave amplitude and spatio-temporal variability

Catheter ablation (CA) is increasingly employed to treat persistent atrial fibrillation (AF), yet assessment of procedural AF termination is still a subject of debate in the medical community. This has motivated the development of different criteria based on the standard electrocardiogram (ECG) to characterize ablation immediate effectiveness. However, most of conventional descriptors are merely computed in one ECG lead, thus neglecting significant information provided by the other leads. The present study proposes a novel predictor of CA outcome by exploiting a subset of the 12 leads in the standard ECG. Our method predicts the need for electrical cardioversion subsequent to CA by suitably combining two sets of multilead features, namely, a measure of fibrillatory wave amplitude and an index of AF spatio-temporal variability per lead. These features are obtained on a reduced-rank approximation determined by principal component analysis emphasizing the highest-variance components in the multilead atrial activity signal, and are then combined by logistic regression. On a database of over 50 persistent AF patients, our method provides reliable predictive measures and proves more robust and informative than classical AF descriptors.

Characteristics of atrial fibrillation cycle length predict restoration of sinus rhythm by catheter ablation

BACKGROUND

Successful termination of atrial fibrillation (AF) during catheter ablation (CA) is associated with arrhythmia-free follow-up. Preablation factors such as mean atrial fibrillation cycle length (AFCL) predict the likelihood of AF termination during ablation but recurring patterns and AFCL stability have not been evaluated.

OBJECTIVE

To investigate novel predictors of acute and postoperative ablation outcomes from intracardiac electrograms: (1) recurring AFCL patterns and (2) localization index (LI) of the instantaneous fibrillatory rate distribution.

METHODS

Sixty-two patients with AF (32 paroxysmal AF; 45 men; age 57 ± 10 years) referred for CA were enrolled. One-minute electrogram was recorded from coronary sinus (CS; 5 bipoles) and right atrial appendage (HRA; 2 bipoles). Atrial activations were detected automatically to derive the AFCL and instantaneous fibrillatory rate (inverse of AFCL) time series. Recurring AFCL patterns were quantified by using recurrence plot indices (RPIs): percentage determinism, entropy of determinism, and maximum diagonal length. AFCL stability was determined by using the LI. The CA outcome predictivity of individual indices was assessed.

RESULTS

Patients with terminated atrial fibrillation (T-AF) had higher RPI (P < .05 in CS7-8) and LI than did those with nonterminated atrial fibrillation (P < .005 in CS3-4; P < .05 in CS5-6, CS7-8, and HRA). Patients free of arrhythmia after 3-month follow-up had higher RPI and LI (all P < .05 in CS7-8). All indices except percentage determinism predicted T-AF in CS7-8 (area under the curve [AUC] ≥ 0.71; odds ratio [OR] ≥ 4.50; P < .05). The median AFCL and LI predicted T-AF in HRAD (AUC ≥ 0.75; OR ≥ 7.76; P < .05). The RPI and LI predicted 3-month follow-up in CS7-8 (AUC ≥ 0.68; OR ≥ 4.17; P < .05).

CONCLUSIONS

AFCL recurrence and stability indices could be used in selecting patients more likely to benefit from CA.