Coupled Tensor Model of Atrial Fibrillation ECG

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

A precise analysis of the atrial activity (AA) signal in electrocardiogram (ECG) recordings is necessary for a better understanding of the mechanisms behind atrial fibrillation (AF). Blind source separation (BSS) techniques have proven useful in extracting the AA source from ECG recordings. However, the automated selection of the AA source among the other sources after BSS is still an issue. In this scenario, the present work proposes two contributions: i) the use of the normalized mean square error of the TQ segment (NMSE-TQ) as a new feature to quantify the AA content of a source, and ii) an automated classification of AA and non-AA sources using three well-known machine learning algorithms. The tested classifiers outperform the techniques present in literature. A pattern in the mean and standard deviation of the used features, for AA and non-AA sources, is also observed.

Atrial fibrillation is the most common sustained cardiac rhythm disturbance, increasing in prevalence with age. During the past 20 years, there has been a 66% increase in hospital admissions related to atrial fibrillation. Neither the natural history of atrial fibrillation nor its response to therapy is sufficiently predictable from clinical and echocardiographic parameters. Treatment of atrial fibrillation is mainly based on trial and error. Thus, it seems appropriate to develop tests that quantify the state of the disease and guide its management. Standard 12-lead electrocardiogram recordings are commonly required for clinical evaluation. Therefore, possible prognostic information contained within the electrocardiogram provides a great interest. The goal of this thesis is to help clinicians treat atrial fibrillation by developing information from the standard 12-lead electrocardiogram on atrial fibrillation substrates, dynamics, and to predict the success of different treatments. Due to the much higher ventricular activity amplitude, the characterization of atrial fibrillation based on surface electrocardiogram signals requires that the ventricular activity first be cancelled. Average beat subtraction and independent component analysis algorithms are the most frequently used techniques in atrial activity extraction. To solve this problem with the best quality results, five different techniques were studied and compared in the first part of this thesis : the use of two different independent component analysis algorithms, a refined version of the average beat subtraction technique, a novel technique that treats each cardiac cycle in an independent manner, and a novel approach based on the use of atom dictionaries dedicated to atrial and ventricular activities. The performance of these five techniques was evaluated by using simulated and clinical electrocardiogram signals. The simulated signals were created by using models of the atria and the thorax. The refined version of the average beat subtraction technique and the technique that treats each cardiac cycle outperformed the other ones and produced high quality results on all leads. Their main advantage lies in the treatment of the ventricular depolarization and repolarization waves in an independent manner. The second part of this thesis aims at exploring the potential of the clean atrial electrocardiogram signals. So far, the complexity of the electrical atrial activity during atrial fibrillation has mainly been assessed through the analysis of the frequency spectrum or of the time-frequency analysis (spectrogram) of the electrocardiogram signal of lead V1. We propose three different approaches to characterize the atrial fibrillation that exploit the information of multiple leads in various and independent manners. Each of them considers different features that require different signal processing techniques. The first approach is based on clinician's observations on electrocardiograms, to characterize the disorganization of fibrillatory waves. We developed the processing tools to quantify these observations. Based on these clinical features, we were able to predict self-termination of atrial fibrillation with a high accuracy. We also observed an interesting correlation between the increase in the percentage of atrial fibrillations identified as non-terminating and the increase in atrial fibrillation duration. The second approach is based on dominant frequencies observed on multiple electrocardiogram signals. Firstly, two simulated cases were analyzed to understand the correspondence between the dominant frequencies observed on the thorax and the atrial fibrillation dynamics. We observed that multiple lead signals yield more information than a single lead in terms of atrial fibrillation dynamic and that lead V1 and V5 were good candidates for the observation of the left and right atrial dynamics, respectively. In order to obtain accurate dominant frequency estimation on clinical signals that contain ventricular artifacts, a recently introduced signal processing technique was tested. We observed a positive left-to-right dominant frequency gradient preference in the overall atrial fibrillations when a negative gradient preference was to be expected based on other invasive studies. These dominant frequency features also permitted us to obtain a good prediction of the response to pharmacological cardioversion attempts. Our third approach was to extract spatial information from the dipole estimated from body surface potentials. This dipole provides a global representation of the electrical atrial activity. Then, features were used to express the complexity of the atrial fibrillation dynamics. This analysis extracted spatial information that was generally lacking in typical non-invasive atrial fibrillation studies. We observed discrimination between atrial fibrillations with both single and recurrent episodes, characterized by modal and uniform dipole distributions, respectively. This discrimination could link the different atrial fibrillation types to the electrical, contractile, and structural remodelings. This approach permitted us to identify and localize stable and single atrial activity sources in the simulations. We hypothesize that this approach, together with patient's anatomical data, could have the power to help clinicians in ablation procedures ; by helping them to predict where ablation lines can be effective.

The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction--e.g., independent component analysis (ICA)--exploit only the spatial diversity introduced by the multiple spatially-separated electrodes. However, AA typically shows certain degree of temporal correlation, with a narrowband spectrum featuring a main frequency peak around 3.5-9 Hz. Taking advantage of this observation, we put forward a novel two-step BSS-based technique which exploits both spatial and temporal information contained in the recorded ECG signals. The spatiotemporal BSS algorithm is validated on simulated and real ECGs from a significant number of atrial fibrillation (AF) and atrial flutter (AFL) episodes, and proves consistently superior to a spatial-only ICA method. In simulated ECGs, a new methodology for the synthetic generation of realistic AF episodes is proposed, which includes a judicious comparison between the known AA content and the estimated AA sources. Using this methodology, the ICA technique obtains correlation indexes of 0.751, whereas the proposed approach obtains a correlation of 0.830 and an error in the estimated signal reduced by a factor of 40%. In real ECG recordings, we propose to measure performance by the spectral concentration (SC) around the main frequency peak. The spatiotemporal algorithm outperforms the ICA method, obtaining a SC of 58.8% and 44.7%, respectively.

The atrial electrocardiogram in patients with persistent atrial fibrillation.

For many patients in persistent Atrial Fibrillation (AF), normal sinus rhythm (NSR) restoration use to be required to reduce the risk of stroke and improve cardiac output. Electrical cardioversion (ECV) is the most effective alternative to revert AF to sinus rhythm. However, because of the high risk of AF recurrence (especially during the first 2 weeks following the procedure) and because of potential collateral effects of ECV, it is clinically important to predict NSR maintenance after ECV before it is attempted. Thereby, the aim of this work is to obtain a robust and non-invasive predictor of NSR maintenance after ECV. The organization degree of the atrial activity (AA) obtained from surface electrocardiographic (ECG) recordings was estimated using a non-linear regularity index because it can be hypothesized that AF recurrence can be greater in those patients who present a highly disorganized AA. With the proposed strategy, 90.24% sensitivity and 78.57% specificity were obtained. Therefore, therapeutic interventions for the treatment of persistent AF could be improved, since unsuccessful electrical cardioversion and the consequent risks for the AF patients could be avoided.

Background Atrial Fibrillation (AF) Burden has been shown to be temporally associated with increased heart failure (HF) hospitalization risk in patients with cardiac implantable electronic devices (CIEDs). AF burden patterns prior to HF events may provide additional information regarding the temporal association. Purpose Investigate the dominating AF burden pattern in CIED patients prior to HF events. Methods Patients with HF events from de-identified Electronic Health Record dataset from 2007-2021 and 120 days of preceding CIED-derived continuous rhythm data from a linked manufacturer’s data warehouse were included. Patients were categorized into three groups based on the occurrence of AF burden of ≥5.5 hours (defined as AF event per earlier studies) on any given day over a 30-day period in 1-30 days before (case period) and 91-120 days (control period) before the first HF event in each patient. Self-organizing map was used to find dominant AF burden patterns in the case and control periods for these three groups of patients. Results A total of 7257 earliest HF events with AF burden available in case and control period was included. Three dominant AF burden patterns were observed in the self-organizing maps analysis: patients with no AF or paroxysmal AF, new onset persistent AF (≥7 continuous days of AF), chronic AF or long-standing persistent AF (≥30 continuous days of AF). Patients with AF burden (a day with ≥5.5 hours) in case period and no AF burden in control (763 patients) have new onset persistent AF in 56% of cases and paroxysmal AF in the rest 44%. Whereas, in patients with AF burden (a day with ≥5.5 hours) in control period but no AF burden in case period (194 patients) had chronic or long-standing persistent AF in 17% and paroxysmal AF in 83%. Finally, patients with a day of AF burden (a day with ≥5.5 hours) in both case and control period (1324 patients) had chronic AF or long-standing persistent AF in 72%, paroxysmal AF in 12%, and new onset persistent AF in 16% of the cases. Conclusion Patients with HF events and significant AF burden have larger proportion of paroxysmal and new onset persistent AF in patients with AF 30 days prior to HF events. Larger proportion of patients with HF events had chronic or long-standing persistent AF in patients who have AF burden more than 90 days before HF event.AF Burden Pattern Chart

Outcomes after stepwise ablation for persistent atrial fibrillation in patients with heart failure

Decrement Evoked Potential (DEEP) Mapping of the Atria: Unmasking Atrial Fibrillation Substrate.

Funding AcknowledgementsType of funding sources: Public grant(s) – EU funding. Main funding source(s): ITEA 3 Symphony projectIntroductionElectro-anatomical maps are standardly used to visualize anatomy and electrophysiological properties of the left atrium during pulmonary vein isolation (PVI) in atrial fibrillation (AF) patients. We sought to describe heterogeneity in atrial voltage and activation time, as a proof-of-principle, to study its association with treatment outcome. Therefore, we projected all data-points from electro-anatomical maps to the CT scan meshes of the left atrium and calculated median voltage and activation time in patients with paroxysmal or persistent AF undergoing first PVI ablation. We hypothesize that the variation and heterogeneity in these parameters throughout the left atrium carry important prognostic and diagnostic information and can be used to discern different subtypes of AF.MethodsWe included AF patients who underwent PVI for the first (cohort I) and second time (cohort II) during 2021-2022. Electro-anatomical maps during sinus rhythm were acquired before and after the ablation procedure and were projected on the patient-specific CT-scan-derived mesh of the left atrium. Patients underwent 6 to 12-month follow-up and were classified as persistent (AF lasting longer than 7 days and usually requiring electrical cardioversion) and paroxysmal (self-terminating AF episodes, lasting shorter than 7 days, mostly shorter than 24 hours). We determined bipolar voltages and activation times for persistent and paroxysmal AF patients in each cohort. In addition, we compared global left atrial voltage and activation times pre- and post-PVI.ResultsOur study population consisted of 24 patients: 14 patients in cohort I and 10 patients in cohort II. Summary of baseline and voltage and activation times can be found in Figure 1. In both cohorts, patients with persistent AF showed a trend towards a lower median LA voltage (Figure 1), but not a longer median atrial activation time. Paroxysmal patients showed more heterogeneity (wider IQR) in both voltage and activation times compared to persistent AF patients. Figure 2 displays a typical example of an electro-anatomical reconstruction of a paroxysmal AF patient pre- and post-second-PVI where the local heterogeneity can be observed - after the ablation, higher voltages and shorter activation times were observed.ConclusionWe provide a proof-of-principle demonstrating that with the use of conventional electro-anatomical maps, important information on heterogeneity and variance in local voltage and activation time can be quantified. This quantification can help discern different AF subtypes and might contribute to understanding different arrhythmogenic substrates and treatment outcome differences.

Although hypertension is associated with atrial fibrillation (AF), the impact of hypertension on the electromechanical properties and outcome of catheter ablation in AF patients is unclear. AF patients [n=213, 136 paroxysmal AF (PAF) patients and 77 persistent AF patients] undergoing circumferential pulmonary vein (PV) isolation guided by CARTO mapping were enrolled, and then were divided into normotension group and hypertension group. Several left atrial (LA) electroanatomical parameters determined by the CARTO system were compared between groups. The LA bipolar voltage was lower in PAF patients with than without hypertension (1.44±1.09 vs. 1.92±0.76 mV, P=0.048); a significant difference was also observed in persistent AF patients. Hypertension significantly increased the size of the LA scar and low-voltage zones (LVZs) in both PAF and persistent AF patients. However, hypertension did not significantly affect recurrence in either PAF or persistent AF patients. The LA bipolar voltage was higher in PAF patients without recurrence than in those with recurrence (1.77±1.01 vs. 1.29±0.93 mV, P=0.048); a significant difference was also observed in persistent AF patients. PAF and persistent AF patients with AF recurrence had significantly larger LA scar and LVZs than patients without recurrence. Hypertension has a significant impact on the LA electromechanical properties in AF patients, and the LA substrate has an important influence on the outcome of catheter ablation.

To evaluate the effectiveness of Ibutilide in cardioversion of persistent atrial fibrillation in patients with sinus node disease wearing a dual chamber pacemaker and to assess the potential role of overdrive ventricular pacing in prevention of drug related proarrhythmia. Fifty-three sinus node disease patients (35 males; mean age 75 +/- 9.5 years), implanted with a dual chamber pacing system, with persistent atrial fibrillation, lasting for 328 +/- 416 days, received 1-2 mg of intravenous Ibutilide. Pacing mode was programmed in VVI at 90 ppm, in order to suppress spontaneous ventricular activity. All patients were monitored for 4 hours. Late occurrence of ventricular arrhythmias was evaluated using the pacemaker memory. Ventricular pacing threshold, spontaneous electrogram amplitude and pacing impedance were measured before and after Ibutilide infusion. Cardioversion to sinus rhythm occurred in twenty-two patients (41.5%). Treatment success was significantly related to shorter atrial fibrillation duration. Paced QT interval duration increased from 412 +/- 36 ms to 481 +/- 40 ms (p < 0.0001), without differences between responders and non responders; QRS width did not change significantly (from 152 +/- 25 ms to 161 +/- 25 ms; p = n.s.). No early or late episodes of sustained or non sustained polymorphic ventricular tachycardia were observed. Pacing and sensing threshold did not show any significant variation. Ibutilide showed a good effectiveness in treating persistent atrial fibrillation in paced patients. Overdrive ventricular pacing may have played a role in preventing drug induced ventricular proarrhythmia. No adverse effect on pacing threshold was observed.

Development of a toolbox for electrocardiogram-based interpretation of atrial fibrillation

Knowledge of the impact of paroxysmal and persistent atrial fibrillation (AF) after catheter ablation on in-hospital outcomes and 30-day readmission remains limited. This study aimed to evaluate the procedural outcomes and 30-day readmission rates among patients with paroxysmal or persistent AF who were hospitalized for AF ablation. Using the Nationwide Readmissions Database, our study included patients aged ≥18 years with AF who were hospitalized and underwent catheter ablation during 2017-2020. Then, we compared the in-hospital procedural outcomes and 30-day readmission rates between patients with paroxysmal and persistent AF, respectively. Our study included 7310 index admissions for paroxysmal AF ablation and 9179 index admissions for persistent AF ablation. According to our analysis, there was no significant difference in procedural complications-namely, cerebrovascular accident, vascular complications, major bleeding requiring blood transfusion, phrenic nerve palsy, pericardial complications, and systemic embolization-between the persistent and paroxysmal AF groups. There was also no significant difference in early mortality between these groups (0.5% vs. 0.7%; P = .22). Persistent AF patients had significantly higher rates of prolonged index hospitalization (9.9% vs. 7.2%; P < .01) and non-home discharge (4.8% vs. 3.1%; P < .01). The 30-day readmission rates were comparable in both groups (10.0% vs. 9.5%; P = .34), with recurrent AF and heart failure being two of the most common causes of cardiac-related readmissions. Catheter ablation among hospitalized patients with paroxysmal or persistent AF resulted in no significant difference in procedural complications, early mortality, or 30-day readmission. This suggests that catheter ablation of AF can be performed with a relatively similar safety profile for both paroxysmal and persistent AF.

This study aimed to investigate the functions of mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) in paroxysmal and persistent atrial fibrillation (AF) patients. A total of 9 left atrial appendage (LAA) tissues were collected from patients with AF (ParoAF patients = 3 and PersAF patients = 3) and donors (n=3). Genes and circRNAs were identified by per kilobase per million reads (RPKM) and number of circular reads/number of mapped reads/read length (SRPBM), respectively. Differentially expressed mRNAs (DE mRNAs), lncRNAs (DE lncRNAs), and circRNAs (DE circRNAs) were identified by | log2 (Fold Change) | ≥ 2 and p-value < 0.05. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein-protein, mRNA-lncRNA, and circRNA-miRNA interaction networks were constructed. In addition, logistic analysis was conducted among AF and circRNAs. A total of 285 (116 up-regulated and 169 down-regulated) and 275 (110 up-regulated and 165 down-regulated) DE mRNAs, 575 (276 up-regulated and 299 down-regulated) and 583 (330 up-regulated and 253 down-regulated) DE lncRNAs, and 83 (48 up-regulated and 35 down-regulated) and 99 (58 up-regulated and 41 down-regulated) circRNAs were detected in ParoAF and PersAF, respectively, as compared with control. MAPK signal pathway as well as voltage-dependent, L type, and alpha 1C subunit calcium channel (CACNA1C) might participate in AF occurrence by preventing atrial parasympathetic remodeling. Collagen type I alpha 1 (COL1A1) and COL1A2 mostly participated in the enriched GO and KEGG terms and connected with most of the DE mRNAs. The expression of chr10: 69902697|69948883 was a protective factor against PersAF after adjusting for age (p=0.022, 95% CI: 0.003-0.634). We found that some mRNAs, lncRNAs, circRNAs, and pathways play essential roles in AF pathogenesis and development. Moreover, one protective factor against PersAF was detected.