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Background Ventricular tachyarrhythmias (VTA) with low and varying signal amplitudes and morphologies may not be successfully identified utilizing traditional implantable cardioverter defibrillator algorithms. Objective Develop and validate a novel algorithm (VF Therapy Assurance, VFTA) to improve detection and timely delivery of high voltage therapy (HVT) for these arrhythmias. Method Arrhythmia detection was simulated on recorded VTA electrograms (EGMs) utilizing Abbott's Merlin.net database. EGMs where an HVT occurred only when VFTA was enabled, or where VFTA provided an HVT >30 seconds earlier than without VFTA were re-adjudicated with physician review. As VFTA never prevents detection or therapy, EGMs where VFTA didn't activate or alter HVT were not adjudicated. Results Among 564,353 recorded VTA EGMs from 20,000 devices, VFTA altered HVT in 105 EGMs from 67 devices. Physician adjudication determined that 81.9% (86/105) of these EGMs were true undertreated VTA episodes and would have received appropriate HVT with VFTA enabled. Furthermore, 65% (56/86) of the episodes were ventricular fibrillation, polymorphic, did not self-terminate during the recording window, or not amenable anti-tachycardia pacing. 87.5% (49/56) would not have elicited HVT without VFTA. Overall, VFTA provided new or earlier appropriate HVT in 0.27% (53/20,000) of devices with an increase in inappropriate HVT in 0.07% (14/20,000) devices. Conclusion The VFTA algorithm successfully identifies VTA missed by traditional detection algorithms, due to undersensed ventricular signals resulting in rate falling below the programmed detection rate. The use of VFTA increases the likelihood of delivering life-saving HVT.

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 previously asymptomatic woman in her 40s presented to the emergency with sudden onset of weakness of the right side upper and lower limbs and slurring of speech. Her symptoms improved entirely within 24 hours. Cardiac examination revealed a loud first heart sound and a short mid-diastolic rumble at the apex. ECG showed normal sinus rhythm. Non-contrast CT of the head done in the emergency was normal. Transthoracic echocardiogram image in apical four-chamber view is shown in figure 1. Figure 1 Echocardiogram in apical four chamber view showing a multilobular mass (arrows) in the left atrium during ventricular …

Sudden cardiac death from lethal arrhythmia is a preventable cause of death. Ventricular fibrillation and tachycardia are shockable electrocardiographic (ECG)rhythms that can respond to emergency electrical shock therapy and revert to normal sinus rhythm if diagnosed early upon cardiac arrest with the restoration of adequate cardiac pump function. However, manual inspection of ECG signals is a difficult task in the acute setting. Thus, computer-aided arrhythmia classification (CAAC) systems have been developed to detect shockable ECG rhythm. Traditional machine learning and deep learning methods are now progressively employed to enhance the diagnostic accuracy of CAAC systems. This paper reviews the state-of-the-art machine and deep learning based CAAC expert systems for shockable ECG signal recognition, discussing their strengths, advantages, and drawbacks. Moreover, unique bispectrum and recurrence plots are proposed to represent shockable and non-shockable ECG signals. Deep learning methods are usually more robust and accurate than standard machine learning methods but require big data of good quality for training. We recommend collecting large accessible ECG datasets with a meaningful proportion of abnormal cases for research and development of superior CAAC systems.

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

Background Atrial fibrillation (AF) is the world’s most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods In our 2-step algorithm, we first calculate the R-R interval variability–based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.

In this era of eHealth, healthcare data has gained a significant importance due to having human survival information. Detection of the atrial fibrillation (AF) from electrocardiogram (ECG) rhythm is a promising area of research because of its critical impact on mortality ratio in all over the world. Although, most of the studies have shown more than 90% results accuracy in terms of specificity (Sp) and sensitivity (Se), yet this accuracy is not sufficient and cannot be considered reliable for AF continuous monitoring due to high ratio of false alarms. Existing works scarcely compare the accuracy of the results generated by a classifier with those of other robust classifiers. Further, the results are needed to be verified with more statistical measures. In this paper, a multiple classifiers-based model is proposed in which the transition between normal sinus rhythm (NSR) and AF are performed on the basis of ventricle activities of the heart. The proposed scheme first extracts several features that are pertinent to AF diagnosis from the ECG data. Later, the classification model categorizes rhythms into classes using statistical techniques. The experimental evaluation is performed on five datasets which include AF Challenge 2017 database, NSR database (NSRDB), NSR RR interval database (NSRDB-2), AF database (AFDB) and long-term AF database (LTAFDB). Based on verification of results using different measures, the proposed scheme outperforms in comparison to the existing systems in terms of area under the curve (AUC), Se, Sp, positive predictive value (PPV), accuracy (ACC), and negative predictive value (NPV) for all datasets. Specifically, the decision tree (DT) obtains 99% AUC, 92% Se and 98% Sp for the AF Challenge 2017 database, which are improved than parallel systems.

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.

Newborn male with symptomatic bradycardia initially diagnosed with complete atrioventricular block. Isoproterenol drip was initiated, and the patient was scheduled for pacemaker implantation. During the hospital course, repeat electrocardiogram and Holter monitor revealed evidence of near continuous blocked atrial bigeminy with occasional aberrantly conducted premature atrial contractions. Flecainide was started, resulting in normal sinus rhythm, and the pacemaker implantation was cancelled.

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.

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based ...

Equine athletes have a pattern of exercise which is analogous to human athletes and the cardiovascular risks in both species are similar. Both species have a propensity for atrial fibrillation (AF), which is challenging to detect by ECG analysis when in paroxysmal form. We hypothesised that the proarrhythmic background present between fibrillation episodes in paroxysmal AF (PAF) might be detectable by complexity analysis of apparently normal sinus-rhythm ECGs. In this retrospective study ECG recordings were obtained during routine clinical work from 82 healthy horses and from 10 horses with a diagnosis of PAF. Artefact-free 60-second strips of normal sinus-rhythm ECGs were converted to binary strings using threshold crossing, beat detection and a novel feature detection parsing algorithm. Complexity of the resulting binary strings was calculated using Lempel-Ziv (‘76 & ‘78) and Titchener complexity estimators. Dependence of Lempel-Ziv ‘76 and Titchener T-complexity on the heart rate in ECG strips obtained at low heart rates (25–60 bpm) and processed by the feature detection method was found to be significantly different in control animals and those diagnosed with PAF. This allows identification of horses with PAF from sinus-rhythm ECGs with high accuracy.

Vasospastic angina is an uncommon cause of cardiac arrest. We describe a patient who presented with sudden cardiac arrest due to severe coronary vasospasm. Telemetry during the event revealed ventricular arrhythmias and asystole followed by spontaneous self-conversion back to normal sinus rhythm. The patient underwent implantable cardioverter-defibrillator therapy. (Level of Difficulty: Beginner.).

Electrocardiogram (ECG) technology plays a vital role in detecting arrhythmia. Numerous achievements have been marked in ECG-related research. Most methods first pre-process ECG signals, then extract features, and finally classify them. Most of the ECG signals used in the related studies were analyzed in specific time intervals or using a fixed number of samples. However, it is not always possible to see significant changes in a short term, and the symptoms of some patients are relatively short-lived. Misjudgments are possible because the ECG signal was not accurately extracted. This study proposes a computer-aided diagnosis (CAD) system for classification of Atrial Fibrillation and Normal Sinus Rhythm based on ECG signals through convolutional neural network. The proposed system considers a single heartbeat, rather than a specific number of seconds. This study eschews the one-dimensional digital ECG signal used in previous studies and uses convolutional neural networks to analyze two-dimensional ECG image. This study explores whether two-dimensional image ECG requires signal filtering. The final classification results in filtered ECG signals is accuracy of 99.23%, sensitivity of 99.71%, and specificity of 98.66%. The best result in non-filtered ECG signals achieves accuracy of 99.18%, sensitivity of 99.31%, and specificity of 99.03%. With no cumbersome artificial settings, the results of this study are comparable to the related studies. The proposed CAD system has high generalizability; it can help doctors to diagnose diseases effectively and reduce misdiagnosis.

One of the rare causes of diffuse T-wave inversion (TWI) in electrocardiogram (ECG) is memory T-waves. This should be considered among the differentials of diffuse TWI in ECG of patients presenting to the emergency department (ED), especially when they have previous episodes of ventricular tachycardia (VT) or pacemaker implantation or Wolff-Parkinson-White syndrome. These TWIs are benign and do not require any treatment. However, it is of paramount importance for the emergency physician to differentiate it from ischemia-related T-wave changes. In the following case series, we report three cases of memory T-waves. Two of the cases had TWI in leads II, III, aVF, and V3 to V6 following reversion of VT. The other patient, with a? VVI (Left ventricle paced, Left ventricle sensed, Inhibition to sensing) pacemaker, had memory T-waves in the ECG taken during normal sinus rhythm. In all the three patients, we considered memory T-waves to be the possible cause of TWI. The electrocardiographic diagnostic criteria for memory T-waves are positive T in lead aVL and positive/isoelectric T in the lead I; and precordial TWI >inferior TWI. These criteria are 92% sensitive and 100% specific. In the following case series, we also provide an algorithmic approach for patients with suspected memory T-waves in their 12-lead ECG when they present to the ED.

In this paper, we present our research which confirms the suitability of the convolutional neural network usage for the classification of single-lead ECG recordings. The proposed method was designed for classifying normal sinus rhythm, atrial fibrillation (AF), non-AF related other abnormal heart rhythms and noisy signals. The method combines manually selected features with the features learned by the deep neural network. The Physionet Challenge 2017 dataset of over 8500 ECG recordings was used for the model training and validation. The trained model reaches an average F1-score 0.71 in classifying normal sinus rhythm, AF and other rhythms respectively.

The occlusion in one of the coronary arteries of the heart leads to the cardiac ailment, myocardial infarction (MI). The localization of MI based on the investigation of the morphology of the multi-lead electrocardiogram (ECG) is the initial task for the diagnosis of this ailment. In this paper, the multiscale convolutional neural network is proposed for the automated localization of MI ailment from multi-lead electrocardiogram (ECG) beats. The Fourier-Bessel (FB) series expansion based empirical wavelet transform (EWT) with fixed order ranges is introduced for the multiscale analysis of multi-lead ECG beat. The FB spectrum of each lead ECG beat is segregated into contiguous segments using the fixed order ranges. Furthermore, the order ranges from these contiguous segments are used to design an empirical wavelet filter bank for the extraction of subband signals from each lead ECG beat. The convolutional neural network (CNN) is used for the classification of various categories of MI as anterior MI (AMI), anterio-lateral MI (ALMI), anterio-septal MI (ASMI), inferior MI (IMI), inferio-lateral MI (ILMI), inferio-posterio-lateral MI (IPLMI) and normal sinus rhythm (NSR). The experimental results reveal that the lower-order range subband signal coupled with CNN attains higher average accuracy values of 99.92%, 99.34%, 99.95%, 99.95%, 99.91%, and 99.86% respectively, for AMI, ALMI, ASMI, IMI, ILMI, and IPLMI classes. The subband signal of multi-lead ECG beats with order range of [1-26] is highly affected during various categories of MI heart disease, and this band signal has higher performance as compared to the existing MI localization approaches.

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.

Given the rarity of significant first-degree atrioventricular block (AVB) after Occlutech Figulla Flex II atrial septal defect occluder (OFF II, Occlutech International AB, Helsingborg, Sweden) placement, its management and predictors of recovery have not been established. We report a 9-year-old boy who developed significant first-degree AVB a day after OFF II deployment. Thereafter, oral prednisolone was initiated at 1.5 mg/kg/day. On day 2 of steroid treatment, normal sinus rhythm without conduction delays was occasionally observed, while the prolonged PR interval on the exercise stress test was ameliorated. After 5 days of steroid administration, normal sinus rhythm without conduction delays was completely achieved. An increase in the frequency of normal sinus rhythm and the absence of conduction delays on electrocardiogram monitoring and exercise stress test play an important role in predicting recovery from prolonged PR interval. Thus, steroid treatment can be effective and should be considered for patients who develop first-degree AVB after undergoing OFF II placement for transcatheter device closure of atrial septal defect.

The use of nasopharyngeal (NP) swab sampling for the detection of various respiratory pathogens has been a standard procedure in medicine for many years. While this is a fairly common procedure, there has been a significant increase in utilization recently due to the SARS-CoV-2 pandemic. We describe a case of a 40-year-old SARS-CoV-2 positive patient with no prior cardiac history who developed asystole while an NP swab was being used to obtain a sample for a SARS-CoV-2 assay. Return of normal sinus rhythm was achieved with chest compressions alone. The incident was deemed to have been an exaggerated vagal response to intranasal stimulation; better known as the trigeminocardiac reflex. This is the first reported case describing asystole during use of an NP swab. This case occurred in a patient with no known cardiac disease and highlights the potential importance of the arrhythmogenic nature of COVID-19 that could potentiate the vagal response in susceptible individuals undergoing NP sampling.

According to World Health Organization (WHO) report an estimated 17.9 million lives are being lost each year due to cardiovascular diseases (CVDs) and is the top contributor to the death causes. 80% of the cardiovascular cases include heart attacks and strokes. This work is an effort to accurately predict the common heart diseases such as arrhythmia (ARR) and congestive heart failure (CHF) along with the normal sinus rhythm (NSR) based on the integrated model developed using continuous wavelet transform (CWT) and deep neural networks. The proposed method used in this research analyses the time-frequency features of an electrocardiogram (ECG) signal by first converting the 1D ECG signals to the 2D Scalogram images and subsequently the 2D images are being used as an input to the 2D deep neural network model-AlexNet. The reason behind converting the ECG signals to 2D images is that it is easier to extract deep features from images rather than from the raw data for training purposes in AlexNet. The dataset used for this research was obtained from Massachusetts Institute of Technology-Boston's Beth Israel Hospital (MIT-BIH) arrhythmia database, MIT-BIH normal sinus rhythm database and Beth Israel Deaconess Medical Center (BIDMC) congestive heart failure database. In this work, we have identified the best fit parameters for the AlexNet model that could successfully predict the common heart diseases with an accuracy of 98.7%. This work is also being compared with the recent research done in the field of ECG Classification for detection of heart conditions and proves to be an effective technique for the classification.

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.

Absztrakt: Akut légzési elégtelenség miatt felvett 70 éves férfi betegnél a felvételi EKG pitvari tachycardiát és makro-T-hullám-alternánst mutatott, amely a sinusrhythmus helyreállása után megszűnt. Az EKG-jelenséget sem QT-megnyúlás, sem heveny ischaemia nem kísérte. Az eset arra hívja fel a figyelmet, hogy makro-T-hullám-alternáns jelentkezhet pitvari tachycardia következtében is, amely esetünkben akut respiratoricus insufficientia miatt lépett fel. Orv Hetil. 2020; 161(7): 275–277.

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.

Atrial fibrillation (AF) is one of the most common arrhythmia diseases, the incidence of which is ascendant with age increase. What’s more, AF is a high-risk factor for stroke, ischemia myocardial and other malignant cardiovascular diseases, which would threaten people’s life significantly. Using a mobile device to screen AF segments is an effective way to reduce the mortality and morbidity of malignant cardiovascular diseases. However, most of existing AF detection methods mainly centered on clinical resting ECG signals and were incapable of processing mobile ECG signals with low signal-to-noise ratio which collected by mobile devices. In this paper, we take advantage of a fully convolutional network variant named U-Net for heart rhythmic information capturing by locating R peak positions as well as calculating RR intervals and a 34-layer residual network for waveform morphological features capturing from ECG signals. Combining both rhythmic information and waveform morphological features, two-layer fully connected networks are employed successively to discriminate AF, normal sinus rhythm , and other abnormal rhythm (other). The extensive experimental results show that our proposed AF our proposed AF screening framework named FRM-CNN can achieve $$F_{1}$$ value of 85.08 ± 0.99% and accuracy of $$87.22 \pm 0.71$$ % on identifying AF segments well without handcraft engineering. Compared with the cutting-edge AF detection methods, the FRM-CNN has more superior performance on monitoring people’s health conditions with mobile devices.

Background: Junctional ectopic tachycardia (JET) is one troublesome supraventricular arrhythmia in postoperative pediatric cardiac surgical patients. Unless treated timely and effectively it may lead to morbidity and even mortality. Aim: To understand the role of Ivabradine in the treatment of JET in postoperative pediatric cardiac surgical patients. Case: We present a postoperative case of complete repair of Tetralogy of Fallot who was in normal sinus rhythm in the preoperative period and developed JET 4 hours later in postoperative period which was persistent despite measures to optimize the internal milieu of the body and conventional antiarrhythmics, that was successfully treated with Ivabradine at a dose of 0.05 mg/kg/12 hourly. Conclusion: Ivabradine can be used to successfully treat postoperative JET in cases ofrefractory to other antiarrhythmic drugs like Digoxin and Amiodarone.

Objetivo: Describir el caso de un paciente masculino de 19 años que presenta bradicardia e hipotensión después de una picadura de abeja haciendo una revisión de la literatura y fisiopatología de los cambios cardiovasculares y electrocardiográficos después de una picadura de abeja. Métodos: Se inspeccionó y aseguró la vía aérea del paciente. Un rastreo de ECG realizado al ingreso reveló ritmo idioventricular a una ritmo de 41 latidos por minuto. Se administró oxígeno, se estableció un acceso IV y se colocaron los cables del marcapasos transcutáneo en el tórax del paciente, luego se indujo el manejo de las guías publicadas. Resultados: El electrocardiograma mostró un ritmo idioventricular a una frecuencia de 41 latidos por minuto que se resolvió a un ritmo sinusal normal después del tratamiento. Conclusiones: Se debe realizar una evaluación cardiovascular completa y rápida en todos los pacientes que se presentan al departamento de emergencias después de una picadura de abeja, y se deben seguir las pautas publicadas sobre el manejo de la bradicardia y la anafilaxia para lograr resultados exitosos.

Background:Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods:The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results:For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions:Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR) versus Atrial Fibrillation (AF) from a single channel short ECG segment. For this purpose, AF and NSR from PhysioNet/Computing in Cardiology Challenge 2017 training dataset were used in this study. RR intervals were extracted using GQRS algorithm, and RR time series with less than 30 beats were excluded from the analysis. The stratified split was applied to create a training set (NSR: 1521 and AF: 239) and test set (NSR: 1527 and AF:234). Shapelets were extracted by scanning RR time series in the training set and identifying statistically significant patterns. For classification, an XGBoost model was trained using the presence or absence of the top 100 shapelets. Using the top 100 significant shapelets (Shapelet length between 5 and 29), we achieved the area under the ROC curve (AUC) and the area under the precession recall curve (AUPRC) of 0.94 and 0.77 in discrimination between AF and NSR. Among the top 100 significant shapelets, we used all shapelets with length no greater than a certain threshold (maximum acceptable Shapelet length) for training different models. Increasing the number of shapelet features by varying the threshold from 5 to 30 in the model training improved AUC/AUPRC 0.91/0.68 to 0.94/0.77

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.

Introduction: An artificial intelligence (AI) algorithm applied to electrocardiography (ECG) during normal sinus rhythm (NSR) has been shown to predict concomitant atrial fibrillation (AF). We sought to characterize the value of AI-ECG as a predictor of future AF and assess its performance compared to other clinical prediction scores in a population-based sample. Methods: We calculated the AI-ECG probability during NSR in patients who enrolled in the population-based Mayo Clinic Study of Aging with at least one ECG in NSR within two years prior and no history of AF at the time of the baseline study visit. The cumulative incidence of AF was estimated for strata defined by AI-ECG probability and CHARGE-AF score. Cox proportional hazards were fit to assess the independent prognostic value and interaction of AI-ECG probability and CHARGE-AF score. Concordance (c) statistics were calculated for AI-ECG probability, CHARGE-AF score and combined AI-ECG and CHARGE-AF score. Results: A total of 1,936 patients with a median age 75.8 (quartile range [QR] 70.4, 81.8) years, median CHARGE-AF score 14.0 (QR 13.2, 14.7) and median CHADS2VASC score 3 (QR 2, 4) were included in the analysis. The cumulative incidence of AF increased in a stepwise fashion across quartiles of AI-ECG probability and CHARGE-AF score (Figure 1). When compared in the same model, both AI-ECG probability (hazard ratio [HR] 1.76, 95% confidence interval [CI] 1.51-2.04) and CHARGE-AF score (HR 1.90, 95% CI 1.58-2.28) independently predicted AF without significant interaction (p=0.54). C statistics were 0.69 (95% CI 0.66-0.72) for AI-ECG probability, 0.69 (95% CI 0.66-0.71) for CHARGE-AF and 0.72 (95% CI 0.69-0.75) for combined AI-ECG and CHARGE-AF score. Conclusions: In the Mayo Clinic Study of Aging, both the AI-ECG probability and CHARGE-AF score independently predicted time to AF. The AI-ECG may offer a means to assess risk with a single test without requiring manual or automated clinical data abstraction.

In this paper, we propose electrocardiogram (ECG) signal processing using mini computer Raspberry Pi 3 Model B (RasPi) for Sudden Cardiac Death (SCD) prediction. AD8232 and TL084 are ICs used for ECG signal processing until R peak was detected. Poincare Plot of RR Interval Differences (PORRID) was used by the RasPi to process the R peak until the PORRID parameters obtained. Fuzzy classification was employed to make decision of SCD probability. The system was tested with 6 data sets of each Normal Sinus Rhythm (NSR) and SCD. The system successfully categorized the 12 data sets according to the information in the database. Furthermore, 10 persons experienced actual implementation and the highest percentage of SCD probability is 36.82%.

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.

Atrial and ventricular fibrillation are considered as frequent cardiac arrhythmias. Therefore, early detection of both fibrillation is important to support medical treatment. In this paper, design of detector circuit for atrial and ventricular fibrillation using R-R interval of electrocardiogram (ECG) signals is reported and considered as a solution for early detection. The detector circuit was designed using easy buy components. The ECG signals were classified as Normal Sinus Rhythm (NSR) when the R-R interval is detected regularly, Atrial Fibrillation (AF) when the R-R interval is detected irregularly, and Ventricular Fibrillation (VF) when the R-R interval is not detected. The system was directly tested using three human bodies for NSR category. Due to some limitations, three recorded ECG signals for each AF and VF were taken from PhysioNet database as samples. The system obtained square waves with amplitude of 5 V where every rising edges indicate R peaks. Experiment results show that the system can classify correctly.

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.

Electrocardiography (ECG) is a widely used tool for studying and diagnosing the heart diseases. Atrial fibrillation (AF) is an irregular and often rapid heart rate that can increase the risk of strokes, heart failure and other heart-related complications. In this study, we develop a novel and effective method to predict the potential AF risk of patients using our ECG signal dataset collected in the University of Illinois Hospital and Health Sciences System. We use a convolutional neural network (CNN) structure to process both the ECG signals and the related health data of patients. Our experimental results indicate that the model with patients’ health data can predict the AF with 79.9% accuracy), and which is better than a CNN trained without related health data 72.2% accuracy), which implies that patients’ health data play an important role in predicting AF risk. Very high sensitivity and specificity of the class of normal sinus rhythm (NSR) cases also verify that the model works well for distinguishing between NSR and ECG signals with potential AF risk.

Sample entropy (SampEn) is widely used for electrocardiogram (ECG) signal analysis to quantify the inherent complexity or regularity of RR interval time series (i.e., heart rate variability (HRV)), with the hypothesis that RR interval time series in pathological conditions output lower SampEn values. However, ectopic beats can significantly influence the entropy values, resulting in difficulty in distinguishing the pathological situation from normal situations. Although a theoretical operation is to exclude the ectopic intervals during HRV analysis, it is not easy to identify all of them in practice, especially for the dynamic ECG signal. Thus, it is important to suppress the influence of ectopic beats on entropy results, i.e., to improve the robustness and stability of entropy measurement for ectopic beats-inserted RR interval time series. In this study, we introduced a physical threshold-based SampEn method, and tested its ability to suppress the influence of ectopic beats for HRV analysis. An experiment on the PhysioNet/MIT RR Interval Databases showed that the SampEn use physical meaning threshold has better performance not only for different data types (normal sinus rhythm (NSR) or congestive heart failure (CHF) recordings), but also for different types of ectopic beat (atrial beats, ventricular beats or both), indicating that using a physical meaning threshold makes SampEn become more consistent and stable.

Atrial Fibrillation or AF is the most common atrial arrhythmia and often occurs especially in the elderly people. Since AF is associated with an increased risk for stroke, regular AF monitoring is recommended for the elderly as well as the patients to reduce the incidence of stroke. In this study, the subsequent two methods of AF detection have been. The first one is a low complexity algorithm with rhythm-based for embedding into portable ECG devices. The second one is morphology-based method using a transferred deep learning with fine-tuning. The performances of the low complexity algorithm are 100% of sensitivity and 86.67% of specificity, respectively while the performances of the deep learning algorithm are 96.97% of sensitivity and 100% of specificity, respectively. From these results, the combination between rhythm-based and morphology-based algorithm can offer the highest sensitivity for screening of AF and highest specificity for separating Normal Sinus Rhythm (NSR) from AF. These developed methods will be useful for improving the performance of AF detection utilized in healthcare system.

Atrial Fibrillation (AFib) is an irregular rhythm of the heart that initiates in the atria. In AFib, the rate of the rhythm of the heart is asynchronous compared to the rate of the rhythm of a healthy heart. The electrical impulses directed from the atria cause a very fast rhythm due to which the atria cannot contract or squeeze blood effectively into the ventricles. Patients suffering from AFib are prone to heart failure, stroke and eventually death. The presence of AFib can be detected by tracking the changes in the P wave of ECG. In this paper, an algorithm is developed that can detect the AFib by using morphological features of atrial activity acquired from ECG. Classification of AFib is done using (SVM) Support Vector Machines. This approach is analyzed for AFib on 44 records including Normal Sinus Rhythm (NSR) and AFib data that are extracted from various sources such as MIT-BIH NSR Database, Intracardiac AFib and AF Termination Challenge. Satisfactory result is obtained for AFib as 91.67% Specificity, 87.5% Sensitivity, and 90.91% Accuracy.

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.

The study used signals of normal rhythm, ventricular arrhythmia, and atrial fibrillation from the following databases: MIT-BIH Atrial Fibrillation Database and RR Normal Sinus Rhythm Database. The article presents the calculation of the senior Lyapunov exponent and analyzes the data obtained.