Atrial Fibrillation Class Research Articles

Abstract 11962: Breaking Down the Relationship Between Atrial Fibrillation Class and Risk of Embolic Stroke: Beyond CHADSVASC Score

Introduction: Atrial fibrillation (AF) is recognized as a risk factor for stroke. To evaluate the risk of stroke in AF patients, the CHADSVASC score is commonly utilized. However, the correlation between AF classification and the risk of embolic stroke beyond the CHADSVASC score remains unclear. Methods: We analyzed data from 488,790 admissions with embolic stroke, out of which 133,429 had atrial fibrillation, between 2016-2020 to examine the association between AF class and risk of embolic stroke. The CHADSVASC score was computed for each patient, with risk factors being assigned point values ranging from 0 to 2 based on their association with embolic stroke. Patients were categorized into one of four AF classes, namely paroxysmal, persistent, long-standing persistent, or permanent. Results: Patients with persistent, long-standing persistent, and permanent AF had a higher risk of embolic stroke compared to those with paroxysmal AF, even after adjusting for CHADSVASC score (OR: 3.15, 95% CI: 3.08-3.22, P=0.000, 3.48, 95% CI: 3.26-3.71, P=0.000, 3.56, 95% CI: 2.84-4.47, P=0.000, OR: 3.21, 95% CI: 3.12-3.30, P=0.000). Conclusion: Based on our study findings, it can be concluded that nonparoxysmal atrial fibrillation is associated with a higher risk of embolic stroke compared to paroxysmal atrial fibrillation , regardless of the CHADSVASC score. Therefore, patients diagnosed with persistent, long-standing persistent, and chronic AF should receive enhanced monitoring and more proactive management strategies to effectively mitigate the risk of embolic stroke.

Abstract 18143: Sudden Death in Heart Failure (HF) With Preserved Ejection Fraction: Clinical Profile and Role of Atrial Fibrillation (AF) and HF NYHA Class

Introduction: Clinical risk factors for sudden death(SD) in patients with HFpEF have not been hitherto well characterized nor is the impact of concomitant atrial fibrillation (AF) on SD risk in HFpEF defined. Methods: We evaluated 1765 HFpEF subjects from the TOPCAT Americas trial to identify potential clinical markers for SD risk. We also examined the role of AF presentation, comparing pts in sinus rhythm (SR, n=1319) to pts in AF at study entry with two different AF presentations (Any AF or AF on baseline ECG) during a mean follow up period of 2.9 years. Results: AF on baseline ECG was independently associated with excess CV mortality (HR 1.46, 95% CI 1.10-1.95, P = 0.009). SD occurred in 77 pts (4.4%) of all HFpEF subjects. 20 SD victims had concomitant AF on baseline ECG (26 % of all SD). SD events were significantly more frequent in males (p=.002), in black or other minority ethnicity (vs. white; p<0.001), diabetics (p=0.024), lower eGFR (p=0.035) and vascular disease (p=0.044). Multivariate Cox analysis identified significant increased SD risk in people with reduced eGFR (HR 0.98, CI: 0.97 to 1.00, p=0.005), reduced risk in females (HR 0.40, 95% CI 0.24,0.65, p<0.001) and increased risk in smokers (HR: 2.16, 95% CI: 1.03 to 4.54, p=0.042). In unmatched or propensity score matched cohorts, neither Any AF or AF on ECG was independently associated with increased SD risk (Figure; p>0.2). SD risk was also similar in early and advanced NYHA class HF. Conclusions: 1. SD risk in HFpEF is influenced by gender, race, lifestyle and systemic comorbidities. 2. In contrast to HF with reduced EF, SD risk is not impacted by concomitant AF, its presentation or HF NYHA class.

The Role of Morphology and Heart Rate Variability Features in Detecting Arrhythmias from Short ECGs

Detecting heart arrhythmias from short Electrocardiogram (ECG) recordings remains challenging since recordings are short and contaminated by noise. ECG morphology features and Heart Rate Variability (HRV) time and frequency domain features are widely used for classifying short ECG recordings. Here we investigate the relative roles of ECG morphology features and HRV time and frequency domain features in classifying short ECG recordings provided by the 2017 PhysioNet/Computing in Cardiology Challenge. The classification is performed separately by four machine learning models: Logistic Regression, Decision Tree, K Nearest Neighbors, and Convolutional Neural Network (CNN). Our best classification score is obtained using the deep learning 1-dimensional CNN model trained on HRV time domain features combined with ECG morphology features. It gives an overall F1 score of 0.70 and 0.73 for the cross validation and hidden test respectively when considering the average classification performance over all 4 categories: Atrial Fibrillation (AF), normal, other arrhythmias, and noisy signal. We found that HRV time domain features play an important role in detecting AF, normal, and other classes, whereas ECG morphology features play a key role in detecting the noisy class. When HRV frequency domain features are combined with HRV time domain features, they do not improve and often degrade classifications of short ECG recordings compared to classifications using only HRV time domain features. Combining ECG morphology features with HRV time domain features leads to a better classification performance for short ECG recordings. Feature-based deep learning could serve as a viable and less expensive approach for ECG classifications.

Multiclass Convolutional Neural Networks for Atrial Fibrillation Classification.

Atrial fibrillation (AF) is a common supraventricular arrhythmia. Its automatic identification by standard 12-lead electrocardiography (ECG) is still challenging. Recently, deep learning provided new instruments able to mimic the diagnostic ability of clinicians but only in case of binary classification (AF vs. normal sinus rhythm-NSR). However, binary classification is far from the real scenarios, where AF has to be discriminated also from several other physiological and pathological conditions. The aim of this work is to present a new AF multiclass classifier based on a convolutional neural network (CNN), able to discriminate AF from NSR, premature atrial contraction (PAC) and premature ventricular contraction (PVC). Overall, 2796 12-lead ECG recordings were selected from the open-source "PhysioNet/Computing in Cardiology Challenge 2021" database, to construct a dataset constituted by four balanced classes, namely AF class, PAC class, PVC class, and NSR class. Each lead of each ECG recording was decomposed into spectrogram by continuous wavelet transform and saved as 2D grayscale images, used to feed a 6-layers CNN. Considering the same CNN architecture, a multiclass classifiers (all classes) and three binary classifiers (AF class, PAC class, and PVC class vs. NSR class) were created and validated by a stratified shuffle split cross-validation of 10 splits. Performance was quantified in terms of area under the curve (AUC) of the receiver operating characteristic. Multiclass classifier performance was high (AF class: 96.6%; PAC class: 95.3%; PVC class: 92.8%; NSR class: 97.4%) and preferable to binary classifiers. Thus, our CNN AF multiclass classifier proved to be an efficient tool for AF discrimination from physiological and pathological confounders. Clinical Relevance-Our CNN AF multiclass classifier proved to be suitable for AF discrimination in real scenarios.

Correlations of Serum MMP-9, TIMP-1, TGF-β and bFGF Levels with Atrial Fibrillation in Patients without Structural Heart Disease

ABSTRACT The researchers aimed in highlighting the correlations of serum MMP-9, TIMP-1, TGF-β1 and bFGF levels with atrial fibrillation (AF) within diseased persons showing no structural heart ailment. A total of 236 eligible diseased persons had been categorized within AF category (n=168) and non-AF category (n=68). Left atrial diameter (LAD), left ventricular end-diastolic dimension (LVEDD) and left ventricular ejection fraction (LVEF) had been measured through cardiac ultrasonography. Comparing concerning non-AF category, AF class had larger LAD and smaller LVEF (P<0.01). MMP-9 and TGF-β1 levels had been higher while TIMP-1 level remained less within AF class as compared to non-AF class (P<0.01). Age, LVEF and left atrial MMP-9/TIMP-1 remained free correlative factors of AF (P<0.01). MMP-9/TIMP-1 remained definitely correlated concerning LAD within AF class (r=0.509, P<0.01). Left atrial MMP-9/TIMP-1 remained higher within diseased persons regarding persistent AF (P<0.01). Left atrial MMP-9/TIMP-1 is correlated with AF within diseased persons having no structural heart ailment, particularly in the case of persistent AF.

Catheter ablation of atrial fibrillation in cardiac amyloidosis.

Cardiac amyloidosis is a progressive infiltrative disease involving deposition of amyloid fibrils in the myocardium and cardiac conduction system that frequently manifests with heart failure (HF) and arrhythmias, most frequently atrial fibrillation (AF), atrial flutter (AFL), and atrial tachycardia (AT). We performed an observational retrospective study of patients with a diagnosis of cardiac amyloid who underwent catheter ablation at our institution between January 1, 2011 and December 1, 2018. Patient demographics, procedural characteristics, and outcomes were determined by manual chart review. A total of 13 catheter ablations were performed over the study period in patients with cardiac amyloidosis, including 10 AT/AF/AFL ablations and three atrioventricular nodal ablations. Left ventricular ejection fraction was lower at the time of AV node ablation than catheter ablation of AT/AF/AFL (23%vs 40%, P=.003). Cardiac amyloid was diagnosed based on the results of preablation cardiac MRI results in the majority of patients (n=7, 70%). The HV interval was prolonged at 60±15ms and did not differ significantly between AV nodal ablation patients and AT/AF/AFL ablation patients (69±18ms vs 57±14ms, P=.36). The majority of patients undergoing AT/AF/AFL ablation had persistent AF (n=7, 70%) and NYHA class II (n=5, 50%) or III (n=5, 50%) HF symptoms, whereas patients undergoing AV node ablation were more likely to have class IV HF (n=2, 66%, P=.014). Arrhythmia-free survival in CA patients after catheter ablation of AT/AF/AFL was 40% at 1 year and 20% at 2 years. Catheter ablation of AT/AF/AFL may be a feasible strategy for appropriately selected patients with early to mid-stage CA, whereas AV node ablation may be more appropriate in patients with advanced-stage CA.

Procedural Patterns and Safety of Atrial Fibrillation Ablation: Findings From Get With The Guidelines-Atrial Fibrillation.

Catheter ablation is an increasingly used treatment for symptomatic atrial fibrillation (AF). However, there are limited prospective, nationwide data on patient selection and procedural characteristics. This study describes patient characteristics, techniques, treatment patterns, and safety outcomes of patients undergoing AF ablation. A total of 3139 patients undergoing AF ablation between 2016 and 2018 in the Get With The Guidelines-Atrial Fibrillation registry from 24 US centers were included. Patient demographics, medical history, procedural details, and complications were abstracted. Differences between paroxysmal and patients with persistent AF were compared using Pearson χ2 and Wilcoxon rank-sum tests. Patients undergoing AF ablation were predominantly male (63.9%) and White (93.2%) with a median age of 65. Hypertension was the most common comorbidity (67.6%), and patients with persistent AF had more comorbidities than patients with paroxysmal AF. Drug refractory, paroxysmal AF was the most common ablation indication (class I, 53.6%) followed by drug refractory, persistent AF (class I, 41.8%). Radiofrequency ablation with contact force sensing was the most common ablation modality (70.5%); 23.7% of patients underwent cryoballoon ablation. Pulmonary vein isolation was performed in 94.6% of de novo ablations; the most common adjunctive lesions included left atrial roof or posterior/inferior lines, and cavotricuspid isthmus ablation. Complications were uncommon (5.1%) and were life-threatening in 0.7% of cases. More than 98% of AF ablations among participating sites are performed for class I or class IIA indications. Contact force-guided radiofrequency ablation is the dominant technique and pulmonary vein isolation the principal lesion set. In-hospital complications are uncommon and rarely life-threatening.

Artificial Neural Network for Atrial Fibrillation Identification in Portable Devices.

Atrial fibrillation (AF) is a common cardiac disorder that can cause severe complications. AF diagnosis is typically based on the electrocardiogram (ECG) evaluation in hospitals or in clinical facilities. The aim of the present work is to propose a new artificial neural network for reliable AF identification in ECGs acquired through portable devices. A supervised fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability (HRV)) in input and discriminating between AF and non-AF classes in output, was created using the repeated structuring and learning (RSL) procedure. RSL_ANN was created and tested on 8028 (training: 4493; validation: 1125; testing: 2410) annotated ECGs belonging to the “AF Classification from a Short Single Lead ECG Recording” database and acquired with the portable KARDIA device by AliveCor. RSL_ANN performance was evaluated in terms of area under the curve (AUC) and confidence intervals (CIs) of the received operating characteristic. RSL_ANN performance was very good and very similar in training, validation and testing datasets. AUC was 91.1% (CI: 89.1–93.0%), 90.2% (CI: 86.2–94.3%) and 90.8% (CI: 88.1–93.5%) for the training, validation and testing datasets, respectively. Thus, RSL_ANN is a promising tool for reliable identification of AF in ECGs acquired by portable devices.

Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

Clinical assessment of AF pattern is poorly correlated with AF burden and post ablation outcomes: A CIRCA-DOSE sub-study

BackgroundContemporary guidelines recommend that atrial fibrillation (AF) be classified into paroxysmal and persistent AF based on clinical assessment, with these categorizations forming the basis of therapeutic recommendations. While pragmatic, clinical assessment may introduce misclassification errors, which may impact treatment decisions. We sought to determine the relationship between AF classification, baseline AF burden, and post-ablation arrhythmia outcomes. DesignThe current study is a sub-analysis of a prospective, parallel-group, multicenter, single-blinded randomized clinical trial. All 346 patients enrolled in CIRCA-DOSE received an implantable cardiac monitor a median of 72 days prior to ablation. AF was classified as low burden paroxysmal, high burden paroxysmal, or persistent based on clinical assessment prior to device implantation. Prior to ablation patients were re-classified using the same definitions based on device monitoring data. Correlation between classifications, AF burden, and post-ablation arrhythmia outcomes were assessed. ResultsThere was poor agreement between clinical and device-based AF classification (Cohen's kappa: 0.192). AF classification derived from pre-ablation continuous monitoring reflected baseline and post-ablation AF burden with greater accuracy and with less overlap between the AF classes (P < 0.01 for all categorical comparisons). Patients objectively classified as “Low Burden” paroxysmal by continuous monitoring data had significantly greater freedom from recurrent AF/AT/AFL compared to those classified as “High Burden” paroxysmal (hazard ratio [HR] 0.57 for AF/AT/AFL recurrence) or persistent AF (HR 0.19 for AF/AT/AFL recurrence). ConclusionsClassification of AF pattern based on pre-ablation continuous cardiac rhythm monitoring better predicted AF burden and freedom from recurrent AF post ablation. Despite the use of standardized definitions, classification of AF based on clinical assessment did not predict baseline AF burden, post ablation AF burden, or freedom from recurrent AF post ablation.Trial registration: ClinicalTrials.govNCT01913522.

Atrial Fibrillation Prediction With Residual Network Using Sensitivity and Orthogonality Constraints

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia. The atrial beat is irregular during AF, which causes blood flow hardly. This may cause blood clot formation and cardioembolic strokes. Computer-aided devices may assist cardiologists in diagnosing heart rhythm disorders better. From this viewpoint, we attempt to identify the premature atrial complexes (PACs) to predict the occurrence of AF by using electrocardiogram (ECG) spectrograms. Convolutional neural networks (CNN) models such as ResNet and Wide-ResNet are used to predict the prelude of AF. Regularization constraints are used to deal with the imbalanced and small number of samples in the minority premature AF class. Sensitivity regularization investigates small variations in premature AF samples. It highlights more representative features that distinguish the PACs from the normal rhythm. On the other hand, orthogonality regularization removes the interference between negatively correlated feature weights. It places constraints on capturing similar patterns with slight differences. This constraint allows convergence to a better feature representation with fewer weight redundancies. We propose a combination of sensitivity and orthogonality penalty terms to the cost function of ResNet to decrease the overfitting and obtain a superior representation. The re-sampling class distribution method is also utilized to mitigate the issue of imbalanced data. The proposed method shows better AF prediction for highly imbalanced data with a small number of samples.

Sistem Cerdas Deteksi Sinyal Elektrokardiogram (EKG) untuk Klasifikasi Jantung Normal dan Abnormal Menggunakan Jaringan Syaraf Tiruan (JST)

This article aims to describe the accuracy of signal processing using neural networks. The design of this final project hardware consists of Arduino Uno, AD8232 module and electrodes. ECG signals obtained from respondents were used as test data for normal ECG signals, while for abnormal class test data the data used were obtained from the research website, namely physionet with atrial fibrillation class. The design process in this system includes the process of data acquisition, training, feature extraction, testing and classification with artificial neural networks. Based on the results of the performance of this device to record ECG signals on respondents obtained normal ECG signals because the results of recorded ECG signals have a similarity in the PQRST wave with a predetermined target. This system can detect the classification of the heart by recognizing the statistical characteristics of the two signal classes and is trained using neural networks. Based on the testing process using an artificial neural network obtained an accuracy of 76.9%.

Association of atrial fibrillation with coronary bed lesions (according to the coronary angiography register)

Aim. To identify the relationship of atrial fibrillation (AF) with features of lesions of the main coronary arteries or primary branches in patients with coronary artery disease (CAD). Material and methods . From the local database of coronary angiography, 6978 patients with hemodynamically significant (50% of the lumen and more) coronary lesions were selected. The main group consisted of 205 patients with AF. To create an age-matched comparison group, 810 patients without AF were selected from the remaining 6773 patients using a method based on equalizing the frequency distributions. Results. Patients with AF accounted for 2,9% of all patients with hemodynamically significant coronary stenosis. In patients with AF, obesity (56,6% vs 44,5%, p=0,002) and severe (III-IV) classes of chronic heart failure (CHF) according to NYHA classification (51,8% vs 22,8%, p<0,001) was more often detected. These patients had a higher mass index of the left ventricular (LV) (170,9±41,2 g/m 2 vs 150,0±34,7 g/m 2 , p<0,001), higher size index of the right ventricle (RV) (13,8±2,1 mm/m 2 vs 13,1±1,3 mm/m2, p<0,001); hemodynamically significant mitral (MR) (49,7% vs 15,9%, p<0,001) and aortic regurgitation (AR) (6,7% vs 1,5%, p<0,001) was also more often detected. Patients with AF more often had lesions of the right coronary artery (RCA) (90,2% vs 82,1%, p=0,005) and calcification of the coronary arteries (24,3% vs 14,8%, p=0,001), less often — the left type of coronary circulation (6,6% against 12,0%, p=0,029). According to the results of multivariate analysis, the presence of significant MR increased the risk of AF by 3,5 times, AR increased by 3,1 times. With CHF worsening to III-IV NYHA classes, the risk of AF increased by 2,1 times, with the presence of RCA — by 80%, obesity of 2 or 3 degrees — by 40%. An increase in the RV size indices for each mm/m2 increased the risk of AF by 14%; in the LV myocardium mass for each g/m 2 — by 0,5%. Conclusion. Angiographic predictor of AF in patients with CAD is the RCA lesion.

Atrial fibrillation identification based on a deep transfer learning approach

Objective: Detection of Atrial fibrillation (AF); as a very common cardiac arrhythmia; is a challenging issue because it is often asymptotic. Most of previous studies were based on feature extraction or training CNNs from scratch. Difficulties in finding appropriate features, requiring an expertise for feature extraction and tuning parameters, and needs for a large amount of labeled data are the most drawbacks of previous studies. The transfer learning is a solution for these problems especially in the medical analysis where available data is limited. The main goal of this study is to investigate the ability of transfer learning method for classification ECG signals into normal, AF, other rhythms and noisy classes. Approach: In our analysis, ECG signals have been transformed to 6 s segments images and fed to three well-known pre-trained models (AlexNet, VGG-16, and ResNet-152). Features have been extracted from different layers of the models and used as the inputs of a classifier for AF detection. Then we have compared the best resulting model to proposed models trained from scratch. The proposed method also investigated the impact of models depth. The algorithms have been trained and validated using the public dataset of 2017 Physionet challenge. Main results: The method achieved average accuracy 87.9, and F-score 86.4 on the validation dataset (publicly available dataset). The second level of extracted features has the highest accuracy for all the pre-trained models in our study. Pre-trained AlexNet outperformed full training CNNs evaluated in this study. Significance: It is shown that the transfer learning method yields good accuracy recognition performance even when source and target datasets are completely different. The results show that the transfer learning is a reliable, accurate and low computational method to classify AF from short ECG signals while requires neither feature selection nor heavy processing steps.

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning

Objective: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors’ entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing in Cardiology Conference. Approach: Our approach involves extracting features from the ECG waveform and training a machine learning classifier. In feature extraction, we calculate several statistical features related to the ECG signal and fiduciary points. We also used a disciplined method of feature selection to reduce the dimensionality of the feature space. We also employ sparse coding as an unsupervised feature extraction tool. The classifier we use is a decision tree-based ensemble learning classifier. Main results: When applied to the hidden test data reserved by the PhysioNet Challenge organizers, our classifier reports F1 scores of 0.91, 0.78, and 0.71 for the Normal, AF, and Other classes, respectively. The overall test score is 0.80, and is obtained by averaging the F1 scores for these three classes. Significance: This work demonstrates that feature selection and ensemble learning can be used to improve the performance of ECG-based classification of AF.

A low-complexity algorithm for detection of atrial fibrillation using an ECG

Objectives: We present a method for automatic processing of single-lead electrocardiogram (ECG) with duration of up to 60 s for the detection of atrial fibrillation (AF). The method categorises an ECG recording into one of four categories: normal, AF, other and noisy rhythm. For training the classification model, 8528 scored ECG signals were used; for independent performance assessment, 3658 scored ECG signals. Approach: Our method was based on features derived from RR interbeat intervals. The features included time domain, frequency domain and distribution features. We assessed the performance of three different classifiers (linear and quadratic discriminant analysis, and quadratic neural network (QNN)) on the training set using 100-fold cross-validation. The QNN was selected as the highest performing classifier, and a further performance assessment on the test data made. Main results: On the test set, our method achieved an F1 score for the normal, AF, other and noisy classes of 0.90, 0.75, 0.68 and 0.32, respectively. The overall F1 score was 0.78. Significance: The computational cost of our algorithm is low as all features are derived from RR intervals and are processed by a single hidden layer neural network. This makes it potentially suitable for low-power devices.

AUTOMATED DETECTION OF ATRIAL FIBRILLATION ECG SIGNALS USING TWO STAGE VMD AND ATRIAL FIBRILLATION DIAGNOSIS INDEX

Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal are not enough to quantify the pathological variations during AF. Therefore, an automated detection of AF pathology using the new diagnostic features of ECG signal is required. This paper proposes a novel method for the detection of AF using ECG signals. In this work, we are using a novel nonlinear method namely, the two-stage variational mode decomposition (VMD) to analyze ECG and deep belief network (DBN) for automated AF detection. First, the ECG signals of both normal sinus rhythm (NSR) and AF classes are decomposed into different modes using VMD. The first mode of VMD is decomposed in the second stage as this mode captures the atrial activity (AA) information during AF. The remaining modes of ECG captures the ventricular activity information. The sample entropy (SE) and the VMD estimated center frequency features are extracted from the sub-modes of AA mode and ventricular activity modes. These extracted features coupled with DBN classifier is able to classify normal and AF ECG signals with an accuracy, sensitivity and specificity values of 98.27%, 97.77% and 98.67%, respectively. We have developed an atrial fibrillation diagnosis index (AFDI) using selected SE and center frequency features to detect AF with a single number. The system is ready to be tested on huge database and can be used in main hospitals to detect AF ECG classes.

Long-Term Outcomes Following High Intensity Focused Ultrasound Ablation for Atrial Fibrillation

The aim of this study is to assess the safety and efficacy of the Epicor high intensity focused ultrasound (St. Jude, Inc.®, Minneapolis, MN, USA) system using seven-day ambulatory electrocardiogram (ECG) monitoring over a two-year follow-up period. One hundred and ten patients undergoing ablation were included from a single center between January 2006 and December 2009. Rhythm was assessed using seven-day ambulatory ECG or permanent pacemaker interrogation. Seventeen patients were lost to follow-up, seven through death. Results were reported according to atrial fibrillation (AF) class preoperatively: paroxysmal, persistent, and long-standing persistent (LSP). Forty-nine percent of patients remained in sinus rhythm at greater than two years. The percentage of patients in sinus rhythm according to preoperative AF class were 81% (paroxysmal AF), 56% (persistent AF), and 18% (long-standing AF). The class of AF prior to surgery, left atrium size, and body mass index determined the long-term outcome. There were no procedure-related complications. We conclude that high intensity focused ultrasound ablation for atrial fibrillation using the Epicor system is safe and effective for surgical patients with paroxysmal AF. The persistent and LSP AF results suggest that alternative ablation strategies should be considered for these patients.

Management of Patients With Atrial Fibrillation (Compilation of 2006 ACCF/AHA/ESC and 2011 ACCF/AHA/HRS Recommendations)

This document is a compilation of the current American College of Cardiology Foundation/American Heart Association (ACCF/AHA) practice guideline recommendations for atrial fibrillation (AF) from the “ACC/AHA/ESC 2006 Guidelines for the Management of Patients With Atrial Fibrillation),”* the “2011 ACCF/AHA/HRS Focused Update on the Management of Patients With Atrial Fibrillation (Updating the 2006 Guideline)”† and the “2011 ACCF/AHA/HRS Focused Update on the Management of Patients With Atrial Fibrillation (Update on Dabigatran).”‡ Updated and new recommendations from 2011 are noted and outdated recommendations have been removed. No new evidence was reviewed, and no recommendations included herein are original to this document. The ACCF/AHA Task Force on Practice Guidelines chooses to republish the recommendations in this format to provide the complete set of practice guideline recommendations in a single resource. ### 1.1. Pharmacological and Nonpharmacological Therapeutic Options #### 1.1.1. Rate Control During AF Class I 1. Measurement of the heart rate at rest and control of the rate using …

ENDOTHELIAL ADHESIVE FUNCTION IN PATIENTS WITH ISCHEMIC HEART DISEASE COMPLICATED BY CHRONIC HEART FAILURE AND ATRIAL FIBRILLATION

Aim. To access the role of endothelial adhesive dysfunction in the development of atrial fibrillation (AF) in patients with ischemic heart disease (IHD) complicated by chronic heart failure (CHF). Material and methods. 111 patients with CHF class I–IV due to myocardial ischemia were included in the study. All participants were divided into four groups according to the presence of AF and CHF class. The control group consisted of 25 healthy individuals. Endothelial adhesive function was assessed by levels of soluble intercellular adhesive molecules sVCAM-1 and sE-selectin. Results. The sVCAM-1 level in patients with CHF class III–IV with AF was significantly higher than this in control group and patients with CHF class I–II regardless of AF occurrence, as well as higher than this in patients with CHF class III–IV without AF (65%, 56.1%, 36.3% and 13.9%, respectively, p<0.05). Direct correlations between sVCAM-1 level and the left atrium volume index (LAVI) (r=0.52; p<0.05) and between sE-selectin level and LAVI (r=0.48; p<0.05) were revealed. Conclusion. Endothelial adhesive dysfunction associates with AF existence and the severity of CHF.