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

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.

F Wave Amplitude as a Predictor of Thromboembolism and Success of Electrical Cardioversion in Patients with Persistent Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is classified according to the amplitude of fibrillatory waves (f) into fine waves (fAF) and coarse waves (cAF).

OBJECTIVES

To correlate the amplitude of f waves with clinical, laboratory, electrocardiographic, and echocardiographic variables that indicate a high risk of thromboembolism and to assess their impact on the success of electrical cardioversion (ECV).

METHODS

Retrospective, observational study that included 57 patients with persistent non-valvular AF who underwent ECV. The maximum amplitude of f waves was measured in lead V1. cAF was defined when f ≥ 1.0mm and fAF when f < 1.0mm. The findings were correlated with the indicated variables. Values of p < 0.05 were considered statistically significant.

RESULTS

cAF (n = 35) was associated with greater success in ECV (94.3% vs. 72.7%, p = 0.036) even after adjusting for variables such as age and BMI (p = 0.026, OR = 11.8). Patients with fAF (n = 22) required more shocks and more energy to revert to sinus rhythm (p = 0.019 and p = 0.027, respectively). There was no significant association between f-wave amplitude and clinical, echocardiographic, and laboratory parameters.

CONCLUSIONS

The amplitude of f wave was not associated with echocardiographic, clinical and laboratory parameters that indicate a high risk of thromboembolism. cAF was associated with a higher chance of success reverting to sinus rhythm employing ECV. A greater number of shocks and energy were required for reversion to sinus rhythm in patients with fAF.

Machine learning based potentiating impacts of 12-lead ECG for classifying paroxysmal versus non-paroxysmal atrial fibrillation

Background

Conventional modality requires several days observation by Holter monitor to differentiate atrial fibrillation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed.

Objective

To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF.

Methods

In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model.

Results

In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteristics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set.

Conclusions

Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model performance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.

A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT

OBJECTIVE

To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF-) post-ablation recurrence and whether these shape differences predict AF recurrence.

METHODS

This retrospective study included 68 AF patients who had pre-catheter ablation cardiac CT scans with contrast. AF recurrence was defined at 1 year, excluding a 3-month post-ablation blanking period. After creating atlases of atrial models from segmented AF+ and AF- CT images, an atlas-based implicit shape differentiation method was used to identify surface of interest (SOI). After registering the SOI to each patient model, statistics of the deformation on the SOI were used to create shape descriptors. The performance in predicting AF recurrence using shape features at and outside the SOI and eight clinical factors (age, sex, left atrial volume, left ventricular ejection fraction, body mass index, sinus rhythm, and AF type [persistent vs paroxysmal], catheter-ablation type [Cryoablation vs Irrigated RF]) were compared using 100 runs of fivefold cross validation.

RESULTS

Differences in atrial shape were found surrounding the pulmonary vein ostia and the base of the left atrial appendage. In the prediction of AF recurrence, the area under the receiver-operating characteristics curve (AUC) was 0.67 for shape features from the SOI, 0.58 for shape features outside the SOI, 0.71 for the clinical parameters, and 0.78 combining shape and clinical features.

CONCLUSION

Differences in left atrial shape were identified between AF recurrent and non-recurrent patients using pre-procedure CT scans. New radiomic features corresponding to the differences in shape were found to predict post-ablation AF recurrence.

Multi-scale Entropy Evaluates the Proarrhythmic Condition of Persistent Atrial Fibrillation Patients Predicting Early Failure of Electrical Cardioversion

Atrial fibrillation (AF) is nowadays the most common cardiac arrhythmia, being associated with an increase in cardiovascular mortality and morbidity. When AF lasts for more than seven days, it is classified as persistent AF and external interventions are required for its termination. A well-established alternative for that purpose is electrical cardioversion (ECV). While ECV is able to initially restore sinus rhythm (SR) in more than 90% of patients, rates of AF recurrence as high as 20-30% have been found after only a few weeks of follow-up. Hence, new methods for evaluating the proarrhythmic condition of a patient before the intervention can serve as efficient predictors about the high risk of early failure of ECV, thus facilitating optimal management of AF patients. Among the wide variety of predictors that have been proposed to date, those based on estimating organization of the fibrillatory (f-) waves from the surface electrocardiogram (ECG) have reported very promising results. However, the existing methods are based on traditional entropy measures, which only assess a single time scale and often are unable to fully characterize the dynamics generated by highly complex systems, such as the heart during AF. The present work then explores whether a multi-scale entropy (MSE) analysis of the f-waves may provide early prediction of AF recurrence after ECV. In addition to the common MSE, two improved versions have also been analyzed, composite MSE (CMSE) and refined MSE (RMSE). When analyzing 70 patients under ECV, of which 31 maintained SR and 39 relapsed to AF after a four week follow-up, the three methods provided similar performance. However, RMSE reported a slightly better discriminant ability of 86%, thus improving the other multi-scale-based outcomes by 3-9% and other previously proposed predictors of ECV by 15-30%. This outcome suggests that investigation of dynamics at large time scales yields novel insights about the underlying complex processes generating f-waves, which could provide individual proarrhythmic condition estimation, thus improving preoperative predictions of ECV early failure.

Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results

OBJECTIVE

This study evaluated the impact of daily ECG (electrocardiogram) self-recordings on time to documented recurrent atrial fibrillation (AF) or atrial flutter (AFL) and time to treatment of recurrent arrhythmia in patients undergoing catheter radiofrequency ablation (RFA) or direct current cardioversion (DCCV) for AF/AFL.

BACKGROUND

AF recurrence rates after RFA and DCCV are 20% to 45% and 60% to 80%, respectively. Randomized trials comparing mobile ECG devices to standard of care have not been performed in an AF/AFL population after treatment.

METHODS

Of 262 patients consented, 238 were randomized to either standard of care (123) or to receive the iHEART intervention (115). Patients in the intervention group were provided with and trained to use an AliveCor KardiaMobile ECG monitor, and were instructed to take and transmit daily ECG recordings. Data were collected from transmitted ECG recordings and patients' electronic health records.

RESULTS

In a multivariate Cox model, the likelihood of recurrence detection was greater in the intervention group (hazard ratio = 1.56, 95% confidence interval [CI]: 1.06-2.30, P = .024). Hazard ratios did not differ significantly for RFA and DCCV procedures. Recurrence during the first month after ablation strongly predicted later recurrence (hazard ratio = 4.53, 95% CI: 2.05-10.00, P = .0006). Time from detection to treatment was shorter for the control group (hazard ratio = 0.33, 95% CI: 0.57-2.92, P < .0001).

CONCLUSIONS

The use of mobile ECG self-recording devices allows for earlier detection of AF/AFL recurrence and may empower patients to engage in shared health decision-making.

A Nurse-led Approach to Improving Cardiac Lifestyle Modification in an Atrial Fibrillation Population

Atrial fibrillation (AF) is a major public health problem and the most common cardiac arrhythmia encountered in clinical practice at this time. AF is associated with numerous symptoms such as palpitations, shortness of breath, and fatigue, which can significantly reduce health-related quality of life and result in serious adverse cardiac outcomes. In light of this, the aim of the present pilot study was to test the feasibility of implementing a mobile health (mHealth) lifestyle intervention titled "Atrial Fibrillation and Cardiac Health: Targeting Improving Outcomes via a Nurse-Led Intervention (ACTION)," with the goal of improving cardiac health measures, AF symptom recognition, and self-management. As part of this study, participants self-identified cardiac health goals at enrollment. The nurse used web-based resources from the American Heart Association (Dallas, TX, USA), which included the Life's Simple 7® My Life Check® assessment, to quantify current lifestyle behavior change needs. Furthermore, on the My AFib Experience™ website (American Heart Association, Dallas, TX, USA), the patient used a symptom tracker tool to capture the date, time, frequency, and type of AF symptoms, and these data were subsequently reviewed by the cardiac nurse. Throughout the six-month intervention period, the cardiac nurse used a motivational interviewing approach to support participants' cardiac health goals. Ultimately, the ACTION intervention was tested in 53 individuals with AF (mean age: 59 ± 11 years; 76% male). Participants were predominantly overweight/obese (79%), had a history of hypertension (62%) or hyperlipidemia (61%), and reported being physically inactive/not preforming any type of regular exercise (52%). The majority (88%) of the participants had one or more Life's Simple 7® measures that could be improved. Most of the participants (98%) liked having a dedicated nurse to work with them on a biweekly basis via the mHealth portal. The most commonly self-reported symptoms were palpitations, fatigue/exercise intolerance, and dyspnea. Seventy percent of the participants had an improvement in their weight and blood pressure as documented within the electronic health record as well as a corresponding improvement in their Life's Simple 7® score at six months. On average, there was a three-pound (1.36-kg) decrease in weight and a 5-mmHg decrease in systolic blood pressure between baseline and at six months. In conclusion, this pilot work provides initial evidence regarding the feasibility of implementing the ACTION intervention and supports testing the ACTION intervention in a larger cohort of AF patients to inform existing AF guidelines and build an evidence base for reducing AF burden through lifestyle modification.

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.

PPARγ agonist use and recurrence of atrial fibrillation after successful electrical cardioversion

Inflammation is associated with atrial fibrillation (AF), and peroxisome proliferator-activated receptor-gamma (PPARγ) agonists have anti-inflammatory properties. We tested whether pioglitazone reduced AF recurrence after electrical cardioversion (EC) by modifying systemic inflammation. In this randomized and prospective trial, patients with persistent AF and type 2 diabetes mellitus were randomized into a pioglitazone group (n=48) or a control group (n=49) before EC. Treatment was continued for 3 months or until AF recurred. Serum inflammatory markers [high sensitivity C-reactive protein (hs-CRP), interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α)] were measured at baseline and follow-up. During the 3-month follow-up, AF recurred in 22 (45.8%) patients of the pioglitazone-treated and 24 (49.0%) patients of the control group (P=0.756). However, the 3 inflammatory markers were significantly lowered with pioglitazone treatment during follow-up. Cox proportional hazards regression models showed that the predictors of recurrence included AF history (relative risk RR 1.002, 95% CI 1.003-1.061, P =0.037) and the left atrial diameter (RR 1.131, 95% CI 1.029-1.242, P = 0.010). In conclusion, while reducing some inflammatory markers, the PPARγ agonist pioglitazone did not affect the recurrence of AF after EC.

Spectral Analysis of Electrocardiograms in Patients with Inducible Atrial Fibrillation after Catheter Ablation Predicts Sinus Rhythm Maintenance

BACKGROUND

We aimed to develop a novel predictive marker for atrial fibrillation (AF) recurrence in patients with inducible AF after catheter ablation, based on power spectral analysis of baseline and postablation electrocardiograms.

METHODS

Twenty-five patients who had undergone their first AF ablation procedure (pulmonary vein isolation and ganglionated plexi ablation) and had inducible AF after ablation were included. A 30-second interval of AF was chosen for each patient before and after ablation, and a periodogram of the atrial activity was computed. A ratio of the power in the dominant frequency to the power in the remainder of the periodogram (DFR) was calculated.

RESULTS

Eight (32%) patients had recurrent AF at 1 year. The clinical and echocardiographic characteristics of patients with and without recurrence were similar (P > 0.05). After ablation, there was organization of atrial activity, evidenced by an increase in the DFR (0.28 ± 0.22 vs 0.53 ± 0.29; P = 0.001). The percent change in DFR before and after ablation (median [interquartile range]) was significantly higher in patients without AF recurrence (120% [30% to 344%] vs 3% [-27% to 66%]; P = 0.01). Receiver operating curve (ROC) analysis demonstrated that a less than 16% increase in DFR postablation was able to predict recurrence of AF (area under ROC curve = 0.82; P = 0.03) with 75% sensitivity and 94% specificity.

CONCLUSION

AF ablation leads to variable organization of atrial activity. Organization of atrial activity after AF ablation is associated with lower 1-year recurrence rates and may be used intraprocedurally after as a novel end point for AF ablation. Larger prospective studies are warranted.

Autonomic influence on atrial fibrillatory process: head-up and head-down tilting

BACKGROUND

Changes in the autonomic nervous system (ANS) tone are present before, during, and after episodes of atrial fibrillation (AF). Atrial fibrillatory rate (AFR, the inverse of the atrial cycle length) has been used as a surrogate marker for local refractoriness and is a key characteristic of the fibrillatory process in patients with AF. Aim of this study is to assess changes in AFR, as an effect of autonomic balance change.

METHODS

Forty patients undergoing cardiac cardioversion for symptomatic persistent AF were included in the study. Surface ECG was recorded during rest, head-down (HDT, -30°), and head-up tilt (HUT, +60°). A median value of AFR was computed in each phase of the protocol.

RESULTS

AFR decreased during HDT compared to the baseline (B) condition in all patients but three (median AFR_B = 391 fpm vs. AFR_HDT = 377 fpm, p < .0001). HUT increased AFR, making it significantly higher than HDT and baseline conditions (median AFR_HUT = 396 fpm, p < .0001 vs. B and HDT). Heart rate (HR) increased during HUT, but had a heterogeneous behavior in the population during HDT: about one third of the patients had an HR lower during HDT than during baseline, whereas the remaining two third had an increase in HR during HDT.

CONCLUSIONS

Dominant sympathetic/vagal tone during HUT/HDT significantly affects AFR, increasing/decreasing in respect to baseline. It may be worth exploring the possibility that patients with AF of shorter duration can convert to sinus rhythm during HDT.

Predictors of successful cardioversion with vernakalant in patients with recent-onset atrial fibrillation

BACKGROUND

Vernakalant is a novel atrial-selective antiarrhythmic drug able to convert recent-onset atrial fibrillation (AF) with reportedly low proarrhythmic risk. Successful cardioversion predictors are largely unknown. We sought to evaluate clinical and electrocardiographic predictors of cardioversion of recent-onset AF with vernakalant.

METHODS

Consecutive patients with AF ≤48 hours admitted for cardioversion with vernakalant (n = 113, median age 62 years, 69 male) were included. Sinus rhythm (SR) within 90 minutes after infusion start was considered to be successful cardioversion. Predictive values of demographics, concomitant therapy, comorbidities, and electrocardiographic parameters were assessed. Atrial fibrillatory rate (AFR), exponential decay, and mean fibrillatory wave amplitude were measured from surface ECG using QRST cancellation and time-frequency analysis.

RESULTS

Cardioversion was achieved in 66% of patients. Conversion rate was higher in women than in men (80% vs 58%, P = 0.02) while none of other clinical characteristics, including index AF episode duration, could predict SR restoration. Female gender was predictive of vernakalant's effect in logistic regression analysis (OR = 2.82 95%CI 1.18-6.76, P = 0.020). There was no difference in AFR (350 ± 60 vs 348 ± 62 fibrillations per minute [fpm], P = 0.893), mean fibrillatory wave amplitude (86 ± 33 vs 88 ± 67 μV, P = 0.852), or exponential decay (1.30 ± 0.42 vs 1.35 ± 0.42, P = 0.376) between responders and nonresponders.

CONCLUSIONS

Female gender is associated with a higher rate of SR restoration using intravenous (i.v.) vernakalant for recent-onset AF. ECG-derived indices of AF organization, which previous studies associated with effect of rhythm control interventions, did not predict vernakalant's effect.

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.

Role of left atrial speckle tracking echocardiography in predicting persistent atrial fibrillation electrical cardioversion success and sinus rhythm maintenance at 6 months

PURPOSE

We assessed the value of left atrium speckle tracking imaging (STI) indices, and clinical and other echocardiographic parameters in persistent atrial fibrillation (AF) patients to predict the efficacy of electrical cardioversion (EC) and sinus rhythm (SR) maintenance at 6 months.

MATERIAL/METHODS

Eighty persistent AF patients planned to receive EC, underwent echocardiography including STI. After 24h, patients with successful EC were examined to predict SR maintenance. After 6 months patients with no AF recurrence in electrocardiogram (ECG) underwent 7-day ECG to exclude silent AF. Every AF>1 min was a recurrence.

RESULTS

SR restored in 61 patients, 19 unsuccessful. Prior use of statins (68.8% vs. 42.1%, p=0.03) was the only factor, determined later by univariate analysis to be a significant EC success predictor (OR=1.09, CL ± 95% 1.001-1.019, p<0.03). Both groups received similar antiarrhythmics and had similar echocardiographic parameters. After 6 months, SR was maintained in 19 patients (31.1%, Group I); AF recurrences were registered in 42 patients (68.8%, Group II). In Group I, only the mitral valve deceleration time (MVDT) 224.18 ± 88.13 vs. 181.6 ± 60.6 in Group II, p=0.04) and the dispersion of time to peak longitudinal strain (dTPLS) (86.0 ± 68.3 vs. 151.8 ± 89.6, p=0.03) differed significantly. The univariate analysis revealed dTPLS as a significant predictor of SR maintenance.

CONCLUSION

High EC efficacy and frequent AF recurrences were observed. The dispersion of time to the maximal longitudinal strain (LS) of left atrial segments facilitated prediction of SR maintenance. The value of 7-day ECG monitoring for detection of arrhythmia after 6 months was limited.

Application of Wavelet Entropy to predict atrial fibrillation progression from the surface ECG

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE) is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG) of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the electrical cardioversion (ECV) outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory (f) waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

Central tendency measure and wavelet transform combined in the non-invasive analysis of atrial fibrillation recordings

Background

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in the clinical practice, being the subject of intensive research.

Methods

The present work introduces two different Wavelet Transform (WT) applications to electrocardiogram (ECG) recordings of patients in AF. The first one predicts spontaneous termination of paroxysmal AF (PAF), whereas the second one deals with the prediction of electrical cardioversion (ECV) outcome in persistent AF patients. In both cases, the central tendency measure (CTM) from the first differences scatter plot was applied to the AF wavelet decomposition. In this way, the wavelet coefficients vector CTM associated to the AF frequency scale was used to assess how atrial fibrillatory (f) waves variability can be related to AF events.

Results

Structural changes into the f waves can be assessed by combining WT and CTM to reflect atrial activity organization variation. This fact can be used to predict organization-related events in AF. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity and accuracy were 100%, 91.67% and 96%, respectively. On the other hand, for ECV outcome prediction, 82.93% sensitivity, 90.91% specificity and 85.71% accuracy were obtained. Hence, CTM has reached the highest diagnostic ability as a single predictor published to date.

Conclusions

Results suggest that CTM can be considered as a promising tool to characterize non-invasive AF signals. In this sense, therapeutic interventions for the treatment of paroxysmal and persistent AF patients could be improved, thus, avoiding useless procedures and minimizing risks.