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Background Atrial fibrillation (AF) is the most prevalent cardiac dysrhythmia with high morbidity and mortality rate. Evidence shows that in every three patients with AF, one is asymptomatic. The asymptomatic and paroxysmal nature of AF is the reason for unsatisfactory and delayed detection using traditional instruments. Research indicates that wearing a dynamic electrocardiogram (ECG) recorder can guide accurate and safe analysis, interpretation, and distinction of AF from normal sinus rhythm. This is also achievable in an upright position and after exercises, assisted by an artificial intelligence (AI) algorithm. Methods This study enrolled 114 participants from the outpatient registry of our institution from June 24, 2020 to July 24, 2020. Participants were tested with a wearable dynamic ECG recorder and 12-lead ECG in a supine, an upright position and after exercises for 60 s. Results Of the 114 subjects enrolled in the study, 61 had normal sinus rhythm and 53 had AF. The number of cases that could not be determined by the wristband of dynamic ECG recorder was two, one and one respectively. Case results that were not clinically objective were defined as false-negative or false-positive. Results for diagnostic accuracy, sensitivity, and specificity tested by wearable dynamic ECG recorders in a supine position were 94.74% (95% CI% 88.76–97.80%), 88.68% (95% CI 77.06–95.07%), and 100% (95% CI 92.91–100%), respectively. Meanwhile, the diagnostic accuracy, sensitivity and specificity in an upright position were 97.37% (95% CI 92.21–99.44%), 94.34% (95% CI 84.03–98.65%), and 100% (95% CI 92.91–100%), respectively. Similar results as those of the upright position were obtained after exercise. Conclusion The widely accessible wearable dynamic ECG recorder integrated with an AI algorithm can efficiently detect AF in different postures and after exercises. As such, this tool holds great promise as a useful and user-friendly screening method for timely AF diagnosis in at-risk individuals.

A 63-year-old man presented to the emergency department for fluttering in the chest. The initial electrocardiogram (ECG) suggested rapid atrial fibrillation with nonspecific RS- and ST-segment abnormalities. Treatment with intravenous β-blocker resulted in conversion to a normal sinus rhythm. The repeat ECG revealed conversion to normal sinus rhythm with an inferior lateral myocardial infarction. This case presentation identifies the importance of an algorithmic approach to identifying narrow complex tachyarrhythmias considering both cardiac and noncardiac causes. Furthermore, evaluating ECG changes when varying rates and rhythms exist on differing ECGs. Identifying the importance of serial ECGs is emphasized by the case presented, concluding with an understanding of arrhythmias associated with myocardial infarctions and clinical pearls.

Purpose: The safe delivery of cardiac radioablation (CR) for atrial fibrillation (AF) is challenged by multi-direction target motion, cardiac rate variability, target proximity to critical structures, and the importance of complete target dose coverage for therapeutic benefit. Careful selection of appropriate treatment procedures is therefore essential. This work characterizes AF cardiac radioablation target motion and target proximity to surrounding structures in both healthy and AF participants to guide optimal treatment technique and technology choice. Methods: Ten healthy participants and five participants with AF underwent MRI acquisition. Multi-slice, cardiac-gated, breath-hold cines were acquired and interpolated to create three-dimensional images for 18-30 cardiac phases. Treatment targets at the left and right pulmonary vein ostia (CTVLeft and CTVRight respectively) and adjacent cardiac structures were contoured and their displacements throughout the cardiac cycle were assessed. Target proximity to surrounding structures were measured. Free-breathing real-time two-dimensional cine images were also acquired at 4 Hz frequency for between 1- and 2-min duration. The motion of easily identifiable points within the target, diaphragm and sternum was measured to assess respiratory motion. Results: Target motion due to cardiac contraction was most prominent in the medial-lateral direction and of 4-5 mm magnitude. CTVRight displacements were smaller in participants with AF than healthy participants in normal sinus rhythm. Nearby cardiac structures often moved with different magnitudes and motion trajectories. CTVLeft and/or CTVRight were in direct contact with the esophagus in 73% of participants. Target motion due to respiration was most prominent in the superior-inferior direction and of 13-14 mm magnitude in both healthy and AF participants. Conclusion: AF CR target motion and relative displacement was characterized. The combination of target motion magnitude and relative displacement to critical structures highlights the importance of personalizing motion compensation techniques for effective AF CR treatments.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

The cellular action potential of cardiac muscles generates Electrocardiogram (ECG) signals. Disturbances in cardiac cells are determined by analyzing the stability of ECG intervals. The PTa Interval (PTaI) of ECG represents the atrial Action Potential Duration (APD) and the evaluation of the causes of PTaI instability can predict the onset of arrhythmia. This study developed an Autoregressive Moving Average with Exogenous Input (ARMAX) model to explore the roles of Heart Rate Variability (HRV) and Premature Activation (PA) in PTaI dynamics using PTaI and PP Interval (PPI) as exogenous inputs. Minute ECG signals were collected from twenty Normal Sinus Rhythm (NSR) and ten Atrial Tachycardia (AT) volunteers. The EDAN PC ECG system was used in the Modified Limb Lead (MLL) configuration to evaluate instability. The instabilities of PTaI were found at the minimum model orders (Amin) of 10 and 11, in the NSR and AT groups, respectively. In the NSR group, the predominant reason for PTaI instability was HRV, whereas among AT patients, it was largely due to PA that preceded the onset of AT. The proposed model showed better prediction of PTaI with minimum Mean Square Error (MSE) between the measured and predicted PTa Intervals (PTaIs). The factor that led to PTaI instability in AT patients was found to be different from that of the NSR group. The frequency of PA (fPA) was found to contribute more in the AT than the NSR group. The developed ARMAX model was better in predicting instability of atrial ECG dynamics in both groups than other autoregressive models currently in use.

Atrial fibrillation (AF) is the most common cardiac arrhythmia with a high incidence of stroke. Many circular RNAs (circRNAs) have been demonstrated they are elated to various heart diseases and may play important roles in diagnostics or many pathophysiological processes. Nevertheless, there is Few studies on circRNAs functions in persistent AF. To identify AF-related circRNAs and construct the integrative regulatory network of circular RNAs, miRNAs, and mRNAs, we collected human right atrial appendage tissues from 5 patients suffering persistent AF (AF group) and 5 patients with normal sinus rhythm (NSR group) and characterized the global changes in circRNA expression with high-throughput sequencing technology. The differential expression of circRNAs and the interactions between circRNAs and microRNAs were analyzed. The microRNA expression file GSE68475 dataset was downloaded from the Gene Expression Omnibus (GEO) database to explore the differentially expressed microRNAs. The target genes of overlapped miRNAs were predicted by using DIANA-TarBase v8. We constructed the circRNA- miRNA-mRNA network using Cytoscape (version 3.4.0) and the network topology was analyzed by utilizing CentiScaPe app. Results showed that all of 600 differentially expressed circRNAs related to AF were screened, including 340 up-regulated and 260 down-regulated circRNAs. An integrative regulatory network was constructed, which included 30 circRNAs, 9 miRNAs and 130 target mRNAs of these miRNAs. It was concluded that that 30 circRNAs, including 8 upregulated circRNAs and 22 downregulated circRNAs, were predicted to highly possibly function as sponges of 9 miRNAs to regulate gene expression by using bioinformatics analysis. Moreover, the interactions of hsa-miR-339-5p with its related circRNAs and target mRNAs constructed the hub regulatory network in persistent AF by utilizing topology analysis. Our proposed regulatory network of circRNAs-miRNAs-mRNAs may provide new insight into the potential mechanism underlying persistent AF. Additionally, these important molecular may become novel biomarkers providing a new strategy in diagnosis and therapy of AF.

Background The factors that influence cognitive function in patients with atrial fibrillation (AF) remain unclear. Methods This study involved an AF group and control group (normal sinus rhythm) of 150 patients each. Cognitive function was assessed with the adjusted Mini-Mental State Examination (MMSEadj) score and Memory and Executive Screening (MES) score. The relationship between cognitive function and the CHA2DS2VASc score was analyzed. Subgroup analysis was performed according to stroke history. Clinical factors affecting the MMSE score were screened by logistic regression analysis. Results Baseline data were similar between the two groups. The MMSEadj and MES scores were significantly lower in the AF than control group; the mean MMSEadj score in the AF non-stroke subgroup and control non-stroke subgroup was 26.2 ± 2.7 and 27.9 ± 2.0, respectively. In non-stroke patients with AF, the MMSEadj and MES scores were negatively correlated with the CHA2DS2VASc score. Factors significantly influencing the MMSE score in these patients were age, education, smoking history, NT-proB-type natriuretic peptide, hemoglobin, and anticoagulation. Conclusion AF is associated with cognitive dysfunction regardless of stroke history. High CHA2DS2VASc scores are associated with impaired cognitive function. Factors influencing cognitive function in non-stroke patients with AF are age, education, smoking history, NT-proB-type natriuretic peptide, hemoglobin, and anticoagulation.

Electrocardiogram (ECG) is a commonly-used, non-invasive examination recording cardiac voltage versus time traces over a period. Deep learning technology, a robust artificial intelligence algorithm, can imitate the data processing patterns of the human brain, and it has experienced remarkable success in disease screening, diagnosis, and prediction. Compared with traditional machine learning, deep learning algorithms possess more powerful learning capabilities and can automatically extract features without extensive data pre-processing or hand-crafted feature extraction, which makes it a suitable tool to analyze complex structures of high-dimensional data. With the advances in computing power and digitized data availability, deep learning provides us an opportunity to improve ECG data interpretation with higher efficacy and accuracy and, more importantly, expand the original functions of ECG. The application of deep learning has led us to stand at the edge of ECG innovation and will potentially change the current clinical monitoring and management strategies. In this review, we introduce deep learning technology and summarize its advantages compared with traditional machine learning algorithms. Moreover, we provide an overview on the current application of deep learning in ECGs, with a focus on arrhythmia (especially atrial fibrillation during normal sinus rhythm), cardiac dysfunction, electrolyte imbalance, and sleep apnea. Last but not least, we discuss the current challenges and prospect directions for the following studies.

A 59-year-old female underwent a dual-chamber pacemaker implantation for intermittent complete heart block. A baseline electrocardiogram showed normal sinus rhythm with first-degree atrioventricular (AV) block and right bundle branch block. A His bundle lead placement was attempted. An intracardiac electrogram from the His bundle lead demonstrated atrial-His, and His-ventricular intervals were 186 and 110 ms, respectively. Pacing was performed from the His bundle lead with a decremental pacing output to assess for the His bundle capture threshold. However, there were no significant QRS morphology changes during the pacing. Is the His bundle captured? The tracing evaluation demonstrated the fascinating physiology of activation wavefront in His Purkinje system that could be applied in the use of conducting system pacing technologies.

Supraventricular tachycardia is a common arrhythmia in pregnancy. During labor and delivery, neuraxial analgesia is important to prevent arrhythmia recurrence. We present the case of a 27-year-old gravida 2 para 1 woman at 35 weeks’ gestation presenting with supraventricular tachycardia that converted to normal sinus rhythm with adenosine. To prevent recurrence of the arrhythmia, an early epidural was provided during labor to minimize catecholamine release.

INTRODUCTION Atrial fibrillation is often asymptomatic and un-diagnosed in the community resulting in an increased risk of heart failure and stroke to those patients. We evaluated the effectiveness, tolerability, and accuracy of a novel six-channel electrocardiogram digital-health screening device, the RhythmPad, for the detection of atrial fibrillation. METHODS Seven hundred and fifty-two participants attending the cardiology department were recruited. Two recordings were taken-a six-lead electrocardiogram using the RhythmPad device and a standard 12-lead electrocardiogram. Recorded traces were analyzed by two blinded cardiologists. The computer-generated automated diagnostic reports from both systems were also compared. Post-participation feedback was obtained from study participants using a three-part questionnaire. RESULTS The sensitivity of the six-lead electrocardiogram compared to the 12-lead electrocardiogram, analyzed by two blinded cardiologists, for the detection of normal sinus rhythm was 95.9%, with a specificity of 97.2%. The sensitivity for the detection of atrial fibrillation using the six-lead ECG was 93.4%, with specificity 96.8%. The six-lead automated diagnostic report had a sensitivity and specificity of 97.5% and 98.6%, respectively, for correctly diagnosing normal sinus rhythm. For the correct diagnosis of atrial fibrillation, the six-lead automated diagnostic report had a sensitivity and specificity of 95.4% and 98.8%, respectively. A total of 95.4% of participants found RhythmPad to be comfortable, with only 0.5% preferring the 12-lead ECG device in comparison to six-lead ECG acquisitions. CONCLUSION The RhythmPad digital health device and its automated diagnostic report were highly accurate in detecting atrial fibrillation when compared to a standard 12-lead electrocardiogram.

Atrial fibrillation (AF) is associated with thrombus formation in the left atrial appendage and systemic embolic events including ischemic stroke. Cardiogenic thromboembolism can also occur in the absence of clinical AF as a result of various pathological conditions affecting the endocardium. The inconsistent temporal relation between AF and ischemic events has stimulated exploration for factors other than clinical AF that contribute to thromboembolism. These include subclinical AF, a thrombogenic atrial cardiomyopathy, and left atrial appendage dysfunction and embolism from other sources. In conclusion, thromboembolism during normal sinus rhythm is likely multifactorial, involving intertwined pathologic processes. Patients at risk, if accurately identified, could theoretically benefit from anticoagulation.

We determined the procedure to reduce arrhythmia-related ghosting artifacts in the late gadolinium enhancement (LGE) imaging of cardiovascular magnetic resonance (CMR) in patients with arrhythmia by examining the causing factors using phantoms. Inversion recovery gradient echo and phase-sensitive inversion recovery (PSIR) sequences were compared under normal sinus rhythm and premature ventricular contraction (PVC) conditions. Under the PVC condition, trigger interval irregularly performed induced ghosting artifacts. A phase-corrected real image in PSIR, however; demonstrated an accurately positive contrast of pale LGE area indicative of mild fibrosis with minimal ghosting artifacts. The study results indicate that PSIR has an advantage for LGE CMR in patients with arrhythmia. Even without having PSIR method, the 2R-R method ensures consistency of contrast and enables reduction of ghost artifacts.

Atrioventricular block in children is not common but is a life-threatening disease. As no spontaneous regression of conductive disruption was reported, those sustaining idiopathic atrioventricular blocks are difficult to manage and often require pacemaker implantation. In this study, we presented the first case of a child who surprisingly recovered from idiopathic complete atrioventricular block without intervention 4 years after initial presentation.

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers.

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincar&eacute, plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.

A detector based only on RR intervals capable of classifying other tachyarrhythmias in addition to atrial fibrillation (AF) could improve cardiac monitoring. In this paper a new classification method based in a 2D non-linear RRI dynamics representation is presented. For this aim, the concepts of Poincare Images and Atlases are introduced. Three cardiac rhythms were targeted: Normal sinus rhythm (NSR), AF and atrial bigeminy (AB). Three Physionet open source databases were used. Poincare Images were generated for all signals using different Poincare plot configurations: $RR$ , $dRR$ and $RRdRR$ . The study was computed for different time window lengths and bin sizes. For each rhythm, the Poincare Images of the 80% of that rhythm's patients were used to create a reference image, a Poincare Atlas. The remaining 20% were used as test set and classified into one of the three rhythms using normalized mutual information and 2D correlation. The process was iterated in a tenfold cross-validation and patient-wise dataset division. Sensitivity results obtained for $RRdRR$ configuration and bin size 40 ms, for a 60 s time window were 94.35% ${}\pm{}$ 3.68, 82.07% ${}\pm{}$ 9.18 and 88.86% ${}\pm{}$ 12.79 with a specificity of 85.52% ${}\pm{}$ 7.46, 95.91% ${}\pm{}$ 3.14, 96.10% ${}\pm{}$ 2.25 for AF, NSR and AB respectively. Results suggest that a rhythms general RRI pattern may be captured using Poincare Atlases and that these can be used to classify other signal segments using Poincare Images. In contrast with other studies, the former method could be generalized to more cardiac rhythms and does not depend on rhythm-specific thresholds.

Introduction Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery. Though often felt to be self-limited, this complication has been associated with increases in both short and long-term stroke and mortality. Several studies have also shown a high rate of AF recurrence. Optimal treatment strategy is not yet defined, and the role of anticoagulation (AC) is unclear. Our objective was to determine provider attitudes toward management of this common complication. Methods A survey consisting of 15 multiple choice questions was distributed to providers at Veterans Healthcare Administration hospitals nationwide. Results The majority of respondents were cardiologists. Practices varied drastically with respect to AC use for patients with POAF who were discharged in normal sinus rhythm. Less variability existed for patients discharged in AF. There was no clear consensus regarding other factors to consider when deciding on AC therapy, including length of episode, or risk factors for stroke such as CHA2 DS2 -VASc score. There was also no consensus on duration of therapy or need for post discharge cardiac monitoring. Conclusion Our data indicate a wide variability in the management of POAF. This reflects conflicting recommendations in the guidelines, as well as a paucity of prospective treatment trials in this field. Nevertheless, a growing evidence base suggests that this complication carries potentially serious long-term morbidity and mortality, and better evidence for its management is needed.

A 42-year old female with no significant past medical history, normal physical exam, and normal routine lab studies presents with “thumps” in her chest. A Holter monitor evaluation revealed normal sinus rhythm with two different PR intervals suggesting “dual AV nodal pathways” and occasional blocked P waves with junctional escape complexes. The behavior was most suggestive that no conduction through either pathway reached the His bundle, suggesting distal block must be beyond each pathway, but still within the AV node. These strips are shown. Their interpretation is discussed, along with the underlying explanation and significance.

BACKGROUND: Atrial fibrillation is the most frequent sustained arrhythmia. Atrial fibrillation often recurs after restoration of normal sinus rhythm. Antiarrhythmic drugs have been widely used to prevent recurrence. This is an update of a review previously published in 2006, 2012 and 2015. OBJECTIVES: To determine the effects of long‐term treatment with antiarrhythmic drugs on death, stroke, drug adverse effects and recurrence of atrial fibrillation in people who had recovered sinus rhythm after having atrial fibrillation. SEARCH METHODS: We updated the searches of CENTRAL, MEDLINE and Embase in January 2019, and ClinicalTrials.gov and WHO ICTRP in February 2019. We checked the reference lists of retrieved articles, recent reviews and meta‐analyses. SELECTION CRITERIA: Two authors independently selected randomised controlled trials (RCTs) comparing any antiarrhythmic drug with a control (no treatment, placebo, drugs for rate control) or with another antiarrhythmic drug in adults who had atrial fibrillation and in whom sinus rhythm was restored, spontaneously or by any intervention. We excluded postoperative atrial fibrillation. DATA COLLECTION AND ANALYSIS: Two authors independently assessed quality and extracted data. We pooled studies, if appropriate, using Mantel‐Haenszel risk ratios (RR), with 95% confidence intervals (CI). All results were calculated at one year of follow‐up or the nearest time point. MAIN RESULTS: This update included one new study (100 participants) and excluded one previously included study because of double publication. Finally, we included 59 RCTs comprising 20,981 participants studying quinidine, disopyramide, propafenone, flecainide, metoprolol, amiodarone, dofetilide, dronedarone and sotalol. Overall, mean follow‐up was 10.2 months. All‐cause mortality High‐certainty evidence from five RCTs indicated that treatment with sotalol was associated with a higher all‐cause mortality rate compared with placebo or no treatment (RR 2.23, 95% CI 1.03 to 4.81; participants = 1882). The number need to treat for an additional harmful outcome (NNTH) for sotalol was 102 participants treated for one year to have one additional death. Low‐certainty evidence from six RCTs suggested that risk of mortality may be higher in people taking quinidine (RR 2.01, 95% CI 0.84 to 4.77; participants = 1646). Moderate‐certainty evidence showed increased RR for mortality but with very wide CIs for metoprolol (RR 2.02, 95% CI 0.37 to 11.05, 2 RCTs, participants = 562) and amiodarone (RR 1.66, 95% CI 0.55 to 4.99, 2 RCTs, participants = 444), compared with placebo. We found little or no difference in mortality with dofetilide (RR 0.98, 95% CI 0.76 to 1.27; moderate‐certainty evidence) or dronedarone (RR 0.86, 95% CI 0.68 to 1.09; high‐certainty evidence) compared to placebo/no treatment. There were few data on mortality for disopyramide, flecainide and propafenone, making impossible a reliable estimation for those drugs. Withdrawals due to adverse events All analysed drugs increased withdrawals due to adverse effects compared to placebo or no treatment (quinidine: RR 1.56, 95% CI 0.87 to 2.78; disopyramide: RR 3.68, 95% CI 0.95 to 14.24; propafenone: RR 1.62, 95% CI 1.07 to 2.46; flecainide: RR 15.41, 95% CI 0.91 to 260.19; metoprolol: RR 3.47, 95% CI 1.48 to 8.15; amiodarone: RR 6.70, 95% CI 1.91 to 23.45; dofetilide: RR 1.77, 95% CI 0.75 to 4.18; dronedarone: RR 1.58, 95% CI 1.34 to 1.85; sotalol: RR 1.95, 95% CI 1.23 to 3.11). Certainty of the evidence for this outcome was low for disopyramide, amiodarone, dofetilide and flecainide; moderate to high for the remaining drugs. Proarrhythmia Virtually all studied antiarrhythmics showed increased proarrhythmic effects (counting both tachyarrhythmias and bradyarrhythmias attributable to treatment) (quinidine: RR 2.05, 95% CI 0.95 to 4.41; disopyramide: no data; flecainide: RR 4.80, 95% CI 1.30 to 17.77; metoprolol: RR 18.14, 95% CI 2.42 to 135.66; amiodarone: RR 2.22, 95% CI 0.71 to 6.96; dofetilide: RR 5.50, 95% CI 1.33 to 22.76; dronedarone: RR 1.95, 95% CI 0.77 to 4.98; sotalol: RR 3.55, 95% CI 2.16 to 5.83); with the exception of propafenone (RR 1.32, 95% CI 0.39 to 4.47) for which the certainty of evidence was very low and we were uncertain about the effect. Certainty of the evidence for this outcome for the other drugs was moderate to high. Stroke Eleven studies reported stroke outcomes with quinidine, disopyramide, flecainide, amiodarone, dronedarone and sotalol. High‐certainty evidence from two RCTs suggested that dronedarone may be associated with reduced risk of stroke (RR 0.66, 95% CI 0.47 to 0.95; participants = 5872). This result is attributed to one study dominating the meta‐analysis and has yet to be reproduced in other studies. There was no apparent effect on stroke rates with the other antiarrhythmics. Recurrence of atrial fibrillation Moderate‐ to high‐certainty evidence, with the exception of disopyramide which was low‐certainty evidence, showed that all analysed drugs, including metoprolol, reduced recurrence of atrial fibrillation (quinidine: RR 0.83, 95% CI 0.78 to 0.88; disopyramide: RR 0.77, 95% CI 0.59 to 1.01; propafenone: RR 0.67, 95% CI 0.61 to 0.74; flecainide: RR 0.65, 95% CI 0.55 to 0.77; metoprolol: RR 0.83 95% CI 0.68 to 1.02; amiodarone: RR 0.52, 95% CI 0.46 to 0.58; dofetilide: RR 0.72, 95% CI 0.61 to 0.85; dronedarone: RR 0.85, 95% CI 0.80 to 0.91; sotalol: RR 0.83, 95% CI 0.80 to 0.87). Despite this reduction, atrial fibrillation still recurred in 43% to 67% of people treated with antiarrhythmics. AUTHORS' CONCLUSIONS: There is high‐certainty evidence of increased mortality associated with sotalol treatment, and low‐certainty evidence suggesting increased mortality with quinidine, when used for maintaining sinus rhythm in people with atrial fibrillation. We found few data on mortality in people taking disopyramide, flecainide and propafenone, so it was not possible to make a reliable estimation of the mortality risk for these drugs. However, we did find moderate‐certainty evidence of marked increases in proarrhythmia and adverse effects with flecainide. Overall, there is evidence showing that antiarrhythmic drugs increase adverse events, increase proarrhythmic events and some antiarrhythmics may increase mortality. Conversely, although they reduce recurrences of atrial fibrillation, there is no evidence of any benefit on other clinical outcomes, compared with placebo or no treatment.

Introduction: Heart failure (HF) affects over 6 million Americans and approximately 650,000 new cases are diagnosed annually, with patients evenly split between HFrEF and HFpEF. Recent advances in therapy for these patients have been limited to pharmaceutical agents, with CRT remaining the most reliable device therapy option since its advent almost twenty years ago. In 2019, after almost two decades without the introduction of a new device therapy for the treatment of moderate HF, the FDA approved CCM® therapy, delivered by the Optimizer Smart device, for patients with NYHA Class III HF who are on guideline-directed medical therapy (GDMT), in normal sinus rhythm (NSR), and with EF ranging from 25% to 45%, and who are ineligible for CRT. Areas covered: Multiple clinical trials support the use of CCM to improve quality of life, functional class, and 6-min hall walk distance. This article will discuss the science behind CCM therapy, the presumed mechanisms of action, the pre-clinical studies that shaped subsequent endeavors, and the clinical trials that support its use. Expert opinion: The introduction of CCM therapy bridges a therapeutic gap for patients with few or no other therapeutic options for NYHA III heart failure.

Background The global age-adjusted mortality rate related to atrial fibrillation (AF) registered a rapid growth in the last four decades, i.e., from 0.8 to 1.6 and 0.9 to 1.7 per 100,000 for men and women during 1990–2010, respectively. In this context, this study uses convolutional neural networks for classifying (diagnosing) AF, employing electrocardiogram data in a general hospital. Methods Data came from Anam Hospital in Seoul, Korea, with 20,000 unique patients (10,000 normal sinus rhythm and 10,000 AF). 30 convolutional neural networks were applied and compared for the diagnosis of the normal sinus rhythm vs. AF condition: 6 Alex networks with 5 convolutional layers, 3 fully connected layers and the number of kernels changing from 3 to 256; and 24 residual networks with the number of residuals blocks (or kernels) varying from 8 to 2 (or 64 to 2). Results In terms of the accuracy, the best Alex network was one with 24 initial kernels (i.e., kernels in the first layer), 5,268,818 parameters and the training time of 89 s (0.997), while the best residual network was one with 6 residual blocks, 32 initial kernels, 248,418 parameters and the training time of 253 s (0.999). In general, the performance of the residual network improved as the number of its residual blocks (its depth) increased. Conclusion For AF diagnosis, the residual network might be a good model with higher accuracy and fewer parameters than its Alex-network counterparts.

A 52-year-old man presented with sudden onset chest pain immediately after eating. The pain was felt strongly in his back and was ‘indescribable’. He arrived via ambulance to the ED clammy, severely nauseated and in 10/10 pain. He had a background of depression and hiatus hernia. No cardiac risk factors. His observations were as follows: HR 100, BP 120/70, Sats 98% on air, lactate 3.6 mmol.L–1. His ECG showed normal sinus rhythm. An …

We propose a novel algorithm to recover fetal electrocardiogram (ECG) for both the fetal heart rate analysis and morphological analysis of its waveform from two or three trans-abdominal maternal ECG channels. We design an algorithm based on the optimal-shrinkage and the nonlocal Euclidean median under the wave-shape manifold model. For the fetal heart rate analysis, the algorithm is evaluated on publicly available database, 2013 PhyioNet/Computing in Cardiology Challenge, set A. For the morphological analysis, we propose to simulate semi-real databases by mixing the MIT-BIH Normal Sinus Rhythm Database and MITDB Arrhythmia Database. For the fetal R peak detection, the proposed algorithm outperforms all algorithms under comparison. For the morphological analysis, the algorithm provides an encouraging result in recovery of the fetal ECG waveform, including PR, QT and ST intervals, even when the fetus has arrhythmia. To the best of our knowledge, this is the first work focusing on recovering the fetal ECG for morphological analysis from two or three channels with an algorithm potentially applicable for continuous fetal electrocardiographic monitoring, which creates the potential for long term monitoring purpose., Comment: 25 pages, 6 figures

Differences in successive R-R intervals (RRIs) were normalized by RRIs before and after the indexing beats (normalized DRs) in individuals with normal sinus rhythm (NSR) and 98.89% of normalized DRs were found to distribute within mean ± 0.100 (≒mean ± 3SD), whereas 73.47% were out of this range in atrial fibrillation (AF). When 7 out 20 normalized DRs fell outside of 0.000 ± 0.100, NSR (n = 129) and AF (n = 108) could be discriminated with high sensitivity, specificity, and predictive values (>99.0% for all). This method will be used in detecting AF candidates from a small number of heart beats or arterial pulses.

Background: Ventricular fibrillation and atrial fibrillation are well-known arrhythmias in patients with Brugada syndrome. This study evaluated the characteristics of the atrial arrhythmogenic substrate using the signal-averaged electrogram (SAECG) in patients with Brugada syndrome. Methods: SAECGs were performed during normal sinus rhythm in 23 normal volunteers (control group), 21 patients with paroxysmal atrial fibrillation (PAF; PAF group), and 21 with Brugada syndrome (Brugada group). Results: The filtered P wave duration (fPd) in the control, Brugada, and PAF groups was 113.9 ± 12.9 ms, 125.3 ± 15.0 ms, and 137.1 ± 16.3 ms, respectively. The fPd in the PAF group was significantly longer compared to that in the control and Brugada groups (p

Electromechanical wave imaging (EWI) is an ultrasound-based technique that can non-invasively map the transmural electromechanical activation in all four cardiac chambers in vivo. The objective of this study was to determine the reproducibility and angle independence of EWI for the assessment of electromechanical activation during normal sinus rhythm (NSR) in healthy humans. Acquisitions were performed transthoracically at 2000 frames/s on seven healthy human hearts in parasternal long-axis, apical four- and two-chamber views. EWI data was collected twice successively in each view in all subjects, while four successive acquisitions were obtained in one case. Activation maps were generated and compared (i) within the same acquisition across consecutive cardiac cycles; (ii) within same view across successive acquisitions; and (iii) within equivalent left-ventricular regions across different views. EWI was capable of characterizing electromechanical activation during NSR and of reliably obtaining similar patterns of activation. For consecutive heart cycles, the average 2-D correlation coefficient between the two isochrones across the seven subjects was 0.9893, with a mean average activation time fluctuation in LV wall segments across acquisitions of 6.19%. A mean activation time variability of 12% was obtained across different views with a measurement bias of only 3.2 ms. These findings indicate that EWI can map the electromechanical activation during NSR in human hearts in transthoracic echocardiography in vivo and results in reproducible and angle-independent activation maps.

SOURCE CITATION Stiell IG, Sivilotti MLA, Taljaard M, et al. A randomized, controlled comparison of electrical versus pharmacological cardioversion for emergency department patients with acute atrial flutter. CJEM. 2021;23:314-24. 33959925.

Atrial fibrillation (AF) monitoring and diagnosis require automatic AF detection methods. In this paper, a novel image-based AF detection method was proposed. The map was constructed by plotting changes of RR intervals (△RR) into grid panes. First, the map was divided into grid panes with 20 ms fixed resolution in y-axes and 15-60 s step length in x-axes. Next, the blank pane ratio (BPR), the entropy and the probability density distribution were processed using linear support-vector machine (LSVM) to classify AF and non-AF episodes. The performance was evaluated based on four public physiological databases. The Cohen's Kappa coefficients were 0.87, 0.91 and 0.64 at 50 s step length for the long-term AF database, the MIT-BIH AF database and the MIT-BIH arrhythmia database, respectively. Best results were achieved as follows: (1) an accuracy of 93.7%, a sensitivity of 95.1%, a specificity of 92.0% and a positive predictive value (PPV) of 93.5% were obtained for the long-term AF database at 60 s step length. (2) An accuracy of 95.9%, a sensitivity of 95.3%, a specificity of 96.3% and a PPV of 94.1% were obtained for the MIT-BIH AF database at 40 s step length. (3) An accuracy of 90.6%, a sensitivity of 94.5%, a specificity of 90.0% and a PPV of 55.0% were achieved for the MIT-BIH arrhythmia database at 60 s step length. (4) Both accuracy and specificity were 96.0% for the MIT-BIH normal sinus rhythm database at 40 s step length. In conclusion, the intuitive grid map of delta RR intervals offers a new approach to achieving comparable performance with previously published AF detection methods.

The rare and potentially fatal complication of asystole during direct laryngoscopy is linked to direct vagal stimulation. This case describes asystole in an 85-year-old female who underwent suspension microlaryngoscopy with tracheal dilation for subglottic stenosis. Quick recognition of this rare event with immediate cessation of laryngoscopy resulted in the return of normal sinus rhythm. This incident emphasizes the implications of continued vigilance during laryngoscopy and the importance of communication between the anesthesia and surgical staff to identify and treat this rare complication. The case was successfully concluded by premedication with an anticholinergic and by increasing the depth of anesthesia.

Grading severity of AS in AF is complicated by varying stroke volumes associated with fluctuating maximum velocities and pressure gradients across the aortic valve. Current guidelines recommend averaging five continuous-wave peak velocity and mean gradient (MG) Doppler signals across the aortic valve when estimating severity of AS in AF. However, it is unknown when grading severity of AS how the average of multiple Doppler signals vs the highest Doppler signal in AF compares to the Doppler signals when the patient is in normal sinus rhythm. We present a series of patients with AS who had two echocardiograms performed within 2-4 months of each other, one when in normal sinus rhythm and one when in AF, and compare the aortic valve hemodynamics associated with the two rhythms.

A man without cardiac symptoms was found to have a slow irregular pulse, and an electrocardiogram revealed sinus bradycardia with escape-capture bigeminy. He was taking verapamil, clonidine, and hydralazine for hypertension. The verapamil was discontinued; he returned to normal sinus rhythm and was discharged on the second hospital day.

The aim of this study is to design GoogLeNet deep neural network architecture by expanding the kernel size of the inception layer and combining the convolution layers to classify the electrocardiogram (ECG) beats into a normal sinus rhythm, premature ventricular contraction, atrial premature contraction, and right/left bundle branch block arrhythmia. Based on testing MIT-BIH arrhythmia benchmark databases, the scope of training/test ECG data was configured by covering at least three and seven R-peak features, and the proposed extended-GoogLeNet architecture can classify five distinct heartbeats; normal sinus rhythm (NSR), premature ventricular contraction (PVC), atrial premature contraction (APC), right bundle branch block (RBBB), and left bundle brunch block(LBBB), with an accuracy of 95.94%, an error rate of 4.06%, a maximum sensitivity of 96.9%, and a maximum positive predictive value of 95.7% for judging a normal or an abnormal beat with considering three ECG segments; an accuracy of 98.31%, a sensitivity of 88.75%, a specificity of 99.4%, and a positive predictive value of 94.4% for classifying APC from NSR, PVC, APC beats, whereas the error rate for misclassifying APC beat was relative low at 6.32%, compared with previous research efforts.

BackgroundAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). ObjectiveWe investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. MethodsA total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. ResultsIn all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). ConclusionsThe algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. Trial RegistrationChinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6)

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients&rsquo, health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

Introduction Atrial fibrillation in postoperative period is common. Although the event of atrial fibrillation is associated with reduced cardiac output and its consequences and cerebrovascular events, its effect on outcome is not clearly documented. This study is done to evaluate the effect of atrial fibrillation on outcome of the operation. Materials and methods This is a retrospective case-control study. A total of 263 patients received coronary artery bypass grafting during this period. The data for demographics, comorbidities, preoperative medications, operative details, and echocardiographic parameters of left ventricular functions were acquired. A total of 24 patients had atrial fibrillation (Group I), while 239 remained in normal sinus rhythm (Group II). The outcome is measured as combined of death and postoperative myocardial infarction (MI). Results The groups are comparable in demographic, preoperative medications, operative, and left ventricular parameters. Of the 24 (9.12%) patients who had postoperative atrial fibrillation, 11 were discharged on medical management. Nine patients reverted to sinus rhythm at discharge. Atrial fibrillation persisted in 8 patients 1 week after discharge and 3 patients after 1 month. The requirement of intraaortic balloon pump (IABP) was statistically significant in group I (5 in group I vs. 10 in group II, n = 0.001). There were 4 deaths in group 1 and 7 in group 2 (P = 0.002), however, the combined end point was achieved in 4 (16.6%) vs. 22 (9.2%), respectively, P = 0.1. Conclusion The appearance of atrial fibrillation heralds increased requirement of IABP, MI, and death in patients undergoing coronary artery bypass grafting.

Background: This study was conducted to investigate factors associated with stroke in pa­tients with paroxysmal atrial fibrillation (PAF) beyond CHADS2 score in terms of left ventricular (LV) diastolic function or left atrial (LA) function. Methods: One hundred and sixty-one patients with PAF and age less than 75 (mean age 61 ± 10; 69 male) who underwent transthoracic echocardiography were investigated. Patients were divided into two groups according to the stroke status (group 1 — no stroke vs. group 2 — presence of stroke). Baseline echocardiographic parameters and LA segmental (4 segments: basal septal, lateral, inferior, and anterior) strain rate (SR) during normal sinus rhythm were analyzed. Results: CHAD score (except S2) was similar between the two groups (0.6 ± 0.7 vs. 0.9 ± 0.7, p = 0.125). Patients with stroke had slightly lower body mass index (24.5 ± 2.7 vs. 23.4 ± ± 2.4, p = 0.052). Echocardiographic parameters did not show any differences in both systolic and diastolic functions between the two groups, however elevated E/E’ ratio was noted (9.5 ± ± 3.8 vs. 11.6 ± 3.9, p = 0.010) due to higher E velocity (63.5 ± 15.9 vs. 70.9 ± 16.0 cm/s, p = 0.046). In the analysis of LA SR, there are no differences of SR among the 4 segments. However, standard deviations (SD) of time to peak SR (SD of tA-SR) of the 4 segments were higher in patients with stroke (10.9 ± 9.9 vs. 22.1 ± 18.1 ms, p = 0.009) which indicates dyssynchronous contraction of LA. In multivariate analysis, SD of tA-SR (OR 1.074, CI 1.024–1.128, p = 0.004) and elevated E/E’ (OR 1.189, CI 1.006–1.406, p = 0.048) were independently associated with stroke in patients with PAF. Conclusions: Elevated E velocity, E/E’ and SD of tA-SR were associated with occurrence of stroke in patients with PAF even with similar CHAD scores. Increased SD of tA-SR and E/E’ were independently associated with stroke in patients with PAF. (

Implantable cardioverter-defibrillators (ICD) provide treatment for life-threatening ventricular tachyarrhythmias. Failure of the pace/sense conductor of an ICD lead can cause noise on the sensing electrogram (EGM) that may be misinterpreted as ventricular activity, triggering inappropriate therapy. An algorithm based upon the confirmation of ventricular activity from a far-field EGM has been developed to reduce inappropriate therapies resulting from this type of lead failure, while ensuring that appropriate therapy is delivered. The objectives of this study were to evaluate the algorithm’s ability to discriminate lead noise from ventricular tachycardia/ventricular fibrillation (VT/VF) and to determine whether it inhibits inappropriate shocks without delaying appropriate shocks. The algorithm was prospectively tested using near- and far-field EGM recordings from patients in three conditions: normal sinus rhythm with sustained and non-sustained lead noise via manipulation of the ICD pocket or lead system, and VT/VF induced during defibrillation threshold testing. The recordings were played through a bench-top device running the algorithm with the diagnosis, time to diagnosis, and inhibition of therapy documented. The algorithm detected noise and withheld inappropriate therapy in 231 of 238 recordings of sustained lead noise that would otherwise have been diagnosed as VT/VF (97.1 %). Non-sustained lead noise was correctly diagnosed in 47 of the 52 recordings (90.4 %). The device appropriately identified all 853 recordings of VT/VF (100 %), without an increase in the time to detection (0.01 ± 0.14 s). The SecureSenseTM algorithm correctly diagnosed sustained and non-sustained lead noise recordings without compromising detection of VT/VF. Use of the algorithm may reduce inappropriate shocks and alert clinicians to lead noise indicative of lead failure.

Introduction Electrocardiographic (EKG) artifacts can lead to unnecessary treatment and costly diagnostic workup. Two previous studies reported a ventricular tachycardia artifact as a result of microdebrider malfunction. In this study, we report the first case of EKG artifacts that mimick asystole from microdebrider use during functional endoscopic sinus surgery (FESS). Case A healthy 19-year-old woman presented with chronic rhinosinusitis, nasal polyposis, and a deviated nasal septum. She was scheduled for a bilateral FESS and septoplasty. During surgery, the microdebrider was changed after 1 hour. While using the second microdebrider, the EKG monitor showed three distinct asystolic events, all lasted approximately 3 seconds. The EKG tracing returned to normal sinus rhythm every time the microdebrider was stopped. The patient's oxygen saturation remained at 100%, and blood pressure remained stable during the episodes. The procedure was aborted, and an extensive cardiology workup was performed, which returned negative results. Biomedical engineering investigated the microdebrider and found a far greater than expected chassis leak, which likely caused electrical interference. Six months later, the patient underwent a right-sided FESS and revision left frontal FESS. There were no EKG abnormalities during the second surgery. Discussion Although other EKG artifacts have been reported in the literature, this is the first case report of an artifact that mimicked asystole that stemmed from microdebrider use. Improved awareness of this potential EKG artifact for both the surgeon and the anesthesiologist may prevent an unnecessary costly workup.

Paroxysmal atrial fibrillation (PAF) has been suggested as a major cause of embolic stroke of undetermined source (ESUS). Transient atrial mechanical dysfunction (stunning) frequently occurs after conversion of atrial fibrillation to sinus rhythm. The study aim was to determine if reversible atrial mechanical dysfunction in ESUS could help elucidate the mechanism of stroke.Eighty-five consecutive patients with acute ischemic stroke were enrolled according to the following inclusion criteria: [1] ≥55 years old; [2] normal sinus rhythm upon admission; [3] no apparent embolic source; and [4] transthoracic echocardiographic evaluation had been performed in both the early phase (72 h) and late phase (7 days) after stroke onset. There were 27 patients in the lacunar or atherothrombotic infarction group (controls), 22 in the PAF group, and 36 in the ESUS group. To determine atrial stunning, transmitral flow velocity profiles (Doppler peak E- [early diastolic] and A- [atrial systolic] waves) were obtained.In the early phase, an E/A velocity ratio ≥ 1.0 was less common in the control group (1 patient, 3.7%) than in the PAF group (19 patients, 86.4%; p 0.001) and ESUS group (10 patients, 27.8%; p 0.05). In the late phase, the E/A ratio decreased to less than 1.0 in six patients (31.6%) who had PAF and in eight patients (80.0%) who had ESUS.Transient atrial mechanical dysfunction could be a helpful finding for elucidating the stroke mechanism in patients with ESUS, and early echocardiographic assessment could improve its detection.

The effect of local atmosphere status and space weather on the healthy volunteers and the patients suffering arterial hypertension (AH) is studied. The research group included 12 healthy volunteers and 15 patients suffering AH of 1–2 degree. In the period of 24-h electrocardiogram monitoring (ECG), all the patients suffering AH continued to take effective antihypertensive therapy individually prescribed. All the patients and volunteers had normal sinus rhythm without disorders in cardiac conduction pathways. The ECG monitoring covered the period from November 23, 2016 till March 29, 2017. During this period, 4 moderate and 11 minor magnetic storms occurred. The horizontal component of the magnetic field recorded ranged from 140 to 270 nT. We used multi-classifier algorithm based on the decision tree with parallel branching, which specially developed by the authors. Apart from the horizontal component of magnetic field, 6 basic characteristics of the local weather data were used as the predictors of the algorithm. The output of this algorithm is elementary statement of cause-consequence connection (insight). In the periods of weak frost and intense precipitations (snow or rain with snow), the combination of rather high horizontal component of the magnetic field with elevated atmospheric pressure and humidity resulted in abnormal RR, PR, and QT intervals of ECG.

The initial surgical attempts to treat atrial fibrillation (AF) were isolation procedures designed to confine the arrhythmia to a specific area of the heart for relief of symptoms. The first surgical attempt to ablate AF was unsuccessful but was quickly followed by the Maze-I procedure on September 25, 1987. Because of several adverse sequelae of the Maze-I procedure, it was sequentially modified to the Maze-II and then Maze-III procedures. The Maze-IV procedure was introduced some 10 years later; these are the only 4 procedures that adhere to the concept of a maze pattern of lesions to ablate AF and leave both atria capable of being activated during normal sinus rhythm. The term maze procedure has become generic for virtually any operation designed to treat AF, but procedures that do not adhere to the concept of creating lesions of conduction block in the pattern of a maze are not maze procedures. These include, among others, the Wolf Mini-Maze, the Left-Sided Maze, and the 5-Box Maze, none of which are truly based on the maze-pattern concept. The cardinal feature of maze procedures that is necessary for both effectiveness and comparability to classical maze procedures includes lines of conduction block that preclude macro-reentry anywhere in either atrium while leaving both atria capable of activation by a sinus-generated impulse. Components essential to achieving this include appropriate lesions in both atria, the absence of gaps that allow electrical activity to bypass an intended line of block, and the absence of alternate pathways by which impulses can reach the intended maze exit.

Background The mechanism of inappropriate sinus tachycardia ( IST ) remains incompletely understood. Methods and Results We prospectively compared 3 patient groups: 11 patients with IST ( IST Group), 9 control patients administered isoproterenol (Isuprel Group), and 15 patients with cristae terminalis atrial tachycardia (AT Group). P‐wave amplitude in lead II and PR interval were measured at a lower and higher heart rate ( HR 1 and HR 2, respectively). P‐wave amplitude increased significantly with the increase in HR in the IST Group (0.16±0.07 mV at HR 1=97±12 beats per minute versus 0.21±0.08 mV at HR 2=135±21 beats per minute, P =0.001). The average increase in P‐wave amplitude in the IST Group was similar to the Isuprel Group ( P =0.26). PR interval significantly shortened with the increases in HR in the IST Group (146±15 ms at HR 1 versus 128±16 ms at HR 2, P PR interval was noted in the Isuprel Group ( P =0.6). In contrast, patients in the atrial tachycardia Group experienced PR lengthening during atrial tachycardia when compared with baseline normal sinus rhythm (153±25 ms at HR 1=78±17 beats per minute versus 179±29 ms at HR 2=140±28 beats per minute, P Conclusions We have shown that HR increases in patients with IST were associated with an increase in P‐wave amplitude in lead II and PR shortening similar to what is seen in healthy controls following isoproterenol infusion. The increase in P‐wave amplitude and absence of PR lengthening in IST support an extrinsic mechanism consistent with a state of sympatho‐excitation with cephalic shift in sinus node activation and enhanced atrioventricular nodal conduction.

Database. The efficiency and robustness of the proposed method has been tested on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), and MIT-BIH Normal Sinus Rhythm Database (MIT-NSD). Aim. Because of the importance of QRS complex in the diagnosis of cardiovascular diseases, improvement in accuracy of its measurement has been set as a target. The present study provides an algorithm for automatic detection of QRS complex on the ECG signal, with the benefit of energy and reduced impact of noise on the ECG signal. Method. The method is basically based on the Teager energy operator (TEO), which facilitates the detection of the baseline threshold and extracts QRS complex from the ECG signal. Results. The testing of the undertaken method on the Fanatasia Database showed the following results: sensitivity (Se) = 99.971%, positive prediction (P+) = 99.973%, detection error rate (DER) = 0.056%, and accuracy (Acc) = 99.944%. On MIT-AD involvement, Se = 99.74%, P+ = 99.97%, DER = 0.291%, and Acc = 99.71%. On MIT-NSD involvement, Se = 99.878%, P+ = 99.989%, DER = 0.134%, and Acc = 99.867%. Conclusion. Despite the closeness of the recorded peaks which inflicts a constraint in detection of the two consecutive QRS complexes, the proposed method, by applying 4 simple and quick steps, has effectively and reliably detected the QRS complexes which make it suitable for practical purposes and applications.

Background Ventriculoatrial shunts are common alternatives for patients who cannot tolerate ventriculoperitoneal shunts. The majority of ventriculoatrial shunt malfunctions are related to mechanical problems. We report an interesting case of ventriculoatrial shunt malfunction due to elevated central venous pressure from new-onset atrial fibrillation. Methods After the patient was confirmed to have ventriculomegaly, he was taken to the operating room for exploration of his ventriculoatrial shunt; there were no obstructions. Subsequently, the patient was cardioverted to normal sinus rhythm for his new onset atrial fibrillation. Results The clinical syndrome and ventriculomegaly both resolved after the patient's atrial fibrillation was corrected with chemical cardioversion. Conclusions The cause of this patient's VA shunt malfunction was likely associated with his new onset atrial fibrillation.

The analysis of P-wave variability from the electrocardiogram (ECG) has been suggested as an early predictor of the onset of paroxysmal atrial fibrillation (PAP). Hence, a preventive treatment could be used to avoid the loss of normal sinus rhythm, thus minimising health risks and improving the patient's quality of life. In these previous studies the variability of different temporal and morphological P-wave features has been only analysed in a linear fashion. However, the electrophysiological alteration occurring in the atria before the onset of PAF has to be considered as an inherently complex, chaotic and non-stationary process. This work analyses the presence of non-linear dynamics in the P-wave progression before the onset of PAF through the application of the central tendency measure (CTM), which is a non-linear metric summarising the degree of variability in a time series. Two hour-length ECG intervals just before the arrhythmia onset belonging to 46 different PAF patients were analysed. In agreement with the invasively observed inhomogeneous atrial conduction preceding the onset of PAF, CTM for all the considered P-wave features showed higher variability when the arrhythmia was closer to its onset. A diagnostic accuracy around 80% to discern between ECG segments far from PAF and close to PAP was obtained with the CTM of the metrics considered. This result was similar to previous P-wave variability methods based on linear approaches. However, the combination of linear and non-linear methods with a decision tree improved considerably their discriminant ability up to 90%, thus suggesting that both dynamics could coexist at the same time in the fragmented depolarisation of the atria preceding the arrhythmia. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved., This work was supported by the projects TEC2014-52250-R from the Spanish Ministry of Economy Competitiveness and PPII-2014-026-P from Junta de Comunidades de Castilla-La Mancha.

The left atrial appendage (LAA) is a well-known source of atrial arrhythmia and atrial fibrillation (AF).The purpose of this study was to determine whether LAA exclusion using the LARIAT device would decrease AF burden.A total of 50 patients with AF and cardiac implantable electronic devices who underwent successful LAA exclusion were enrolled in this prospective observational study. AF burden before LAA exclusion (baseline) and 3 and 12 months after exclusion was assessed by device interrogation.AF burden at 3-month follow-up (42% ± 34%) was significantly lower compared to baseline (76% ± 33%, P.0001). The reduction in AF burden was sustained at 12 months (59% ± 26%, P.001). Subgroup analysis revealed that AF burden at 3-month follow-up was similarly reduced in both paroxysmal AF (n = 19) and nonparoxysmal AF (n = 31). However, there was no reduction in AF burden in patients with paroxysmal AF at 12 months. AF burden in patients with known AF triggers in the LAA (n = 9) was significantly reduced at 3 months (52% ± 35%) and 12 months (42% ± 19%) compared to respective baseline (84 ± 31%, P.0001).LAA exclusion appears to reduce AF burden. The presence of AF triggers in the LAA appears to be the strongest predictor of AF reduction. The study underscores the role of the LAA in arrhythmogenesis for AF and highlights the complementary role of LAA exclusion in restoration of normal sinus rhythm.

We report the case of a female neonate born at 40 weeks' gestation with no known risk factors. On postnatal day 27, she was transferred to the pediatric intensive care unit and intubated for cardiorespiratory failure; she was diagnosed with group B streptococcus infective endocarditis. Mitral valve vegetations did not improve with antibiotic therapy. Mitral valve surgery was performed on postnatal day 36. A mechanical mitral valve with short-segment polytetrafluoroethylene skirt was inserted into the supra-annular position; gentian violet was applied to the sewing ring and skirt. She was discharged from the hospital after 6 weeks with good cardiac function and normal sinus rhythm.

Les tachycardies à QRS large mal tolérées du nourrisson posent le problème de leur diagnostic et de la prise en charge en urgence. Nous rapportons un cas de tachycardie à QRS large chez un nourrisson de 35 jours reçu pour détresse cardio-circulatoire. Le cœur était morphologiquement normal à l’échographie cardiaque Doppler. Un traitement par une dose charge d’Amiodarone n’a pas permis de réduire cette tachycardie. Un retour en rythme sinusal a été obtenu après cardioversion par un défibrillateur externe semi-automatique type Lifeline. Un traitement d’entretien par Amiodarone per os est institué et le patient est en rythme sinusal à 03 mois.