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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 …

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.

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.

Aims Paroxysmal supraventricular tachycardia (PSVT) is not detected owing to its paroxysmal nature, but it is associated with the risk of cardiovascular disease and worsens the patient quality of life. A deep learning model (DLM) was developed and validated to identify patients with PSVT during normal sinus rhythm in this multicentre retrospective study. Methods and results This study included 12 955 patients with normal sinus rhythm, confirmed by a cardiologist. A DLM was developed using 31 147 electrocardiograms (ECGs) of 9069 patients from one hospital. We conducted an accuracy test with 13 753 ECGs of 3886 patients from another hospital. The DLM was developed based on residual neural network. Digitally stored ECG were used as predictor variables and the outcome of the study was ability of the DLM to identify patients with PSVT using an ECG during sinus rhythm. We employed a sensitivity map method to identify an ECG region that had a significant effect on developing PSVT. During accuracy test, the area under the receiver operating characteristic curve of a DLM using a 12-lead ECG for identifying PSVT patients during sinus rhythm was 0.966 (0.948–0.984). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of DLM were 0.970, 0.868, 0.972, 0.255, and 0.998, respectively. The DLM showed delta wave and QT interval were important to identify the PSVT. Conclusion The proposed DLM demonstrated a high performance in identifying PSVT during normal sinus rhythm. Thus, it can be used as a rapid, inexpensive, point-of-care means of identifying PSVT in patients.

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.

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.

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.).

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.

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.

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.

Although various changes in electrocardiogram (ECG) were reported with normal serum potassium levels in acute intracranial pathology, giant asymmetrical T-wave change has not been reported. We report a case of a previously healthy male child who presented with acute febrile illness and features of raised intracranial pressure. Full standardized ECG shows normal sinus rhythm and tall, broad, and giant asymmetrically peaked T-wave. Serum potassium, echocardiography, and cardiac injury marker were normal. The child managed with supportive care and antimicrobials and showed recovery in 7 days.

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.

BACKGROUND Tiletamine-zolazepam is a widely used as an alternative chemical immobilization method in non-human primates, with a safe application pathway and necessary relaxation. In order to determine the electrocardiographic parameters of Sapajus libidinosus after intramuscular tiletamine-zolazepam administration at the dose of 5 mg/kg, nine animals were submitted to anesthesia. METHODS The interpretation of the electrocardiogram determined: heart rate in bpm and heart rhythm, P wave, P-R interval, R wave, QRS complex, T wave, Q-T interval, corrected QT interval and electrical axis. The mean HR was 206 ± 32 bpm. RESULTS The majority of the monkeys showed normal sinus rhythm, but one animal showed sinus tachycardia. The most commonly observed electrical axis was between +30° and +90°. Two animals presented bigger alterations in the tracing such as low amplitude QRS and T wave bigger than 1/4 of the R wave. CONCLUSIONS The administration of tiletamine-zolazepam was safe and efficacious, with minimal cardiovascular effects.

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.

In the field of biomedicine, time irreversibility is used to describe how imbalanced and asymmetric biological signals are. As an important feature of signals, the direction of time is always ignored. To find out the variation regularity of time irreversibility of heart rate variability (HRV) in the initial stage of hypoxic exposure, the present study implemented 2 h acute normobaric hypoxic exposure on six young subjects who have no plateau or hypoxia experiences; oxygen concentration was set as 12.9%. Electrocardiogram (ECG) signals were recorded in the whole process and RR interval sequences were extracted. Mathematical operations were executed to transform the difference of adjacent RR intervals into proportion and distance with delay time to conduct time irreversibility analysis of HRV. The same calculating method was implemented on six items randomly picked out from the MIT-BIH normal sinus rhythm database as a control group. Results show that variation of time irreversibility of HRV in a hypoxic environment is different from that in a normoxic environment, time irreversibility indices of a hypoxic group decreases continually at a delay time of 1 and 2, and indices curves of time irreversibility gradually tend to be steady and gather with each other at a delay time of 3 or 4. The control group shows no consistent tendency no matter what the delay time is in the range of 1–4. Our study indicates that in short-time hypoxic exposure, as hypoxic time goes by, regulation of the cardiovascular autonomic nervous system weakens; regulation times and intensity of sympathetic and parasympathetic nerves tend to be equal.

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.

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.

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.

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.

OnBehalf The ARIC Study Background Age-related left atrial (LA) structural/functional abnormalities in elderly patients with normal sinus rhythm and preserved ejection fraction may precede the development of overt atrial fibrillation, and also may be related to stroke. Purpose To evaluate the association of 3-dimensional echocardiographic (3DE) atrial contractility parameters with subclinical cerebral infarcts (SCIs), as assessed by brain MRI, and clinically diagnosed stroke Methods We studied 407 participants (mean age 76 ± 5 years, 40.5% male) from the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS) without AF and who underwent a brain MRI and a 3DE examination in 2011-13. We defined 3 groups: those with no cerebral infarcts on brain MRI (NCIs, N = 315); those with MRI-diagnosed SCIs (N = 58); and those with clinically diagnosed stroke (N = 34). Results While still within the normal range, LA indexed volume significantly increased across the 3 groups (P-trend = 0.01).This was accompanied by an increase in the LA global longitudinal strain (GLS), an echocardiographic index of LA reservoir function (P-trend = 0.004). E/e’ divided by LA GLS—index of atrial stiffness—worsened across groups (P-trend = 0.005) and was independently associated with SCIs and Stroke, pooled together, [OR per 1 %-1, 1.97; 95% CI (1.24, 3.11), P = 0.004], and Stroke [OR per 1 %-1, 2.30; 95% CI (1.23, 4.30), P = 0.009]. LA GLS was marginally associated with an increased odd of SCIs [OR per 1 %, 1.07; 95% CI (1.01, 1.13), P = 0.014]. Conclusions Among elderly participants with normal sinus rhythm and preserved ejection fraction in a large cohort study, markers of LA function and stiffness are associated with increased odds of subclinical infarcts and stroke. These data suggest that even subtle LA dysfunction, which may contribute to LA stasis, may predispose to subclinical cerebral infarcts and stroke.

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.

Narrowing of coronary arteries caused by cholesterol deposits deprives heart tissues of oxygen. In prolonged conditions, these will result in myocardium infarction. The presence of damage tissues modifies the normal sinus rhythm and this can be detected using electrocardiogram (ECG). Hence, this paper characterized history of myocardial infarction from survivors using QRS power ratio features from the ECG. Subsequent profiling is performed using multilayered perceptron (MLP) and hybrid multilayered perceptron (HMLP) networks. ECG with history of anterior and inferior infarctions, along with healthy controls is obtained from PTB Diagnostic ECG Database. The signal is initially pre-processed and the power ratio features are extracted for low- and mid-frequency components. The features are then used as input vector to the MLP and HMLP networks. The optimized MLP has attained accuracies of 99.2% for training and 98.0% for testing. Meanwhile, the optimized HMLP managed to achieve accuracies of 99.4% for training and 97.8% for testing. Despite the similarities in network performance, MLP provides a better alternative due to the reduced computational requirements by as much as 30%.

Background: Rheumatic heart disease has a strong association with mitral valve stenosis. Atrial fibrillation is one of the most common complications of this condition and is a poor prognostic factor. Early detection and prompt management of atrial fibrillation can help to improve the quality of life and increase the life expectancy of the patients. We carried out this study to investigate the significance of left atrial volumetric changes in mitral stenosis and its correlation with atrial fibrillation. Methodology: We audited the data of 60 patients of rheumatic heart disease who had mitral valve stenosis. The patients were randomized into atrial fibrillation (Group A) and normal sinus rhythm (Group B). We conducted this cross-sectional analytical study at Cardiology Department, Mayo Hospital, Lahore, from 1st February 2017 to 31st January 2018. We only included those patients who consented to be a part of this study and fulfilled our predefined inclusion criteria. Left atrial volume was measured by prolate ellipse method and biplane methods on echocardiography. The Data was analyzed on SPSS v20. Results: Sixty patients were included in the study. Among the subjects, thirty-six (60%) were males, and twenty-four (40%) were females. Atrial fibrillation was noted in 43.33% of the patients of mitral valve stenosis. There was a marked difference in the mean volume of the left atrium among the two groups. We observed that the mean area of the mitral valve for Group A patients was larger than that of patients in Group B. Our study showed an inverse correlation between left atrial volume and mitral valve area among Group A patients. Conclusion: Patients of mitral stenosis are at an increased risk of developing atrial fibrillation if the left atrial volume is increasing. All patients with mitral stenosis should have routine echocardiography & measurement of left atrial volumes, so that proper treatment can be started if the left atrial volume is increasing, to prevent atrial fibrillation.

Implantable pacemakers and cardioverter defibrillators have dramatically improved the lives of countless patients over the past 50 years. Pacemaker functions include atrioventricular sensing and pacing options to mimic normal sinus rhythm, burst pacing based on algorithms to treat atrial reentry tachycardia, and biventricular pacing to coordinate ventricular contraction. Implantable cardioverter defibrillators terminate dangerous rapid heart rhythms by delivering an electric shock. Implantable cardiac devices have expanded the clinician’s ability to treat complex life-threatening arrhythmias, with approximately 400,000 devices implanted each year in the United States. At the present time, there are over three million patients with implanted cardiac devices.

Introduction The National Clinical Programme for Acute Coronary Syndrome in Ireland has successfully improved outcomes for patients presenting with STEMI since its introduction in 2010.1 Appropriate activation of the PPCI protocol is crucial to ensure timely reperfusion for patient with STEMI, while ensuring resources are not over-utilised for patients not requiring urgent PPCI. The aim of this review therefore was to establish the current rate of appropriate activation and false activation for the PPCI service in Galway. Methods Phone records from the dedicated STEMI PPCI telephone number for the Galway PPCI centre were compared to simultaneously transmitted ECGs over a period from 01/01/2019 to 31/03/2019 inclusive (figure 1, table 1). A phone call record corresponding with a transmitted ECG for a new patient was considered activation of the PPCI protocol. These records were compared to the local STEMI database for patients transferred direct to the cardiology team for PPCI. All activations had the corresponding ECG assessed for STEMI criteria as per the ESC guidelines. Results 171 activations of the STEMI PPCI service were recorded from 01/01/2019 to 31/03/2019 inclusive and 71 (42%) were accepted direct to Cardiology for management: 58 patients (34%) were correctly diagnosed with STEMI, 13 patients (7%) were transferred to the direct care of the cardiology team. Five patients (3%) underwent immediate angiography and had no coronary artery disease. Of the 100 patients not transferred directly to cardiology, 61 (36%) patients had ECGs which met ESC ECG criteria2 for STEMI but did not have clinical symptoms consistent with STEMI: LBBB (n=30, 18%), RBBB (n=17, 10%), ST segment elevation (n=8, 5%), paced rhythms (n=6, 4%). 39 patients (23%) had neither ECGs consistent with STEMI nor symptoms requiring urgent transfer for PPCI: Non-specific changes or T-wave inversions (n=30, 18%), normal sinus rhythm (n=7, 5%) and Wellen’s pattern (n=2, 1%). 67 of 100 false activations occurred during on-call hours. The national ambulance service activated the PPCI protocol for 127 cases, of which 39 (31%) cases were correctly diagnosed as STEMI. 52 (39%) patients had ECGs consistent with ESC ECG criteria[2] for STEMI without symptoms and 29 (23%) had neither ECG criteria nor symptoms. In total 44 activations were received from peripheral hospitals, 19 of which were correctly diagnosed as STEMI (43%). 9 (21%) patients had ECG changes consistent with ESC ECG criteria[2] for STEMI without symptoms, and 10 patients(23%) had neither ECG criteria nor symptoms. Conclusion This review identifies important subgroups of patients who were referred for PPCI, in particular 23% of patients who did not require discussion, and 36% of patients who should have discussion with cardiology services before initiating transfer to the catheterisation laboratory.

Introduction Electrode misplacement can adversely affect Electrocardiogram (ECG) morphology and as a consequence, it can affect interpretation and patient diagnosis. Lead misplacement in ECG acquisition can occur in 40% to 60% of the time; which can result. in false positives (Type-I errors) and false negatives (Type-II errors). V1 and V2 electrodes are commonly misplaced high and wide of their correct positions. Vertically misplaced leads V1 and V2 can conceal real STEMI (ST Elevation Myocardial Infarction) events or emulate a false positive. Lead misplacement detection plays an important role to improve ECG data quality and clinical decision making in cardiac care. The aim of the current work was to develop a computer model using deep learning to detect misplacement of leads V1 and V2 for three different types of patients: 1) old Myocardial Infarction (MI) patients, 2) Left Ventricular Hypertrophy (LVH) Patients and 3) normal sinus rhythm patients, and to find in which patient type the ‘general’ patient-type-agnostic computer model achieves the highest performance. Methods 12-lead ECGs for 453 subjects, (normal n=151, LVH n=151, MI n=151) were extracted from body surface potential maps. V1 and V2 leads acquired from the second intercostal space (ICS) are considered as incorrect placement and leads from the fourth (IC) are considered as correct. The computer model was developed using deep learning algorithm (bidirectional long short-term memory network (BLSTM)) to detect V1 and V2 misplacement. The computer model was trained on three datasets together and tested on each dataset separately. In each dataset, data were divided into 67% for training and 33% for testing. The prevalence was 50% for correct and incorrect leads. Results Computer model accuracy using BLSTM of V1 and V2 misplacement detection was 93.0% in normal dataset, 93.0% in MI dataset while the accuracy was 91.0% in LVH dataset. Conclusion Computer model got a high performance to detect V1 and V2 misplacement in normal and MI ECGs datasets, while the performance decreased in LVH dataset to 91.0%. In future we need to consider MI infarct sites such as anterior, inferior etc to increase accuracy.

Atrial fibrillation (AF) is the most common form of arrhythmia with accelerated and irregular heart rate (HR), leading to both heart failure and stroke and being responsible for an increase in cardiovascular morbidity and mortality. In spite of its importance, the direct effects of AF on the arterial hemodynamic patterns are not completely known to date. Based on a multiscale modelling approach, the proposed work investigates the effects of AF on the local arterial fluid dynamics. AF and normal sinus rhythm (NSR) conditions are simulated extracting 2000 $\mathrm{RR}$ heartbeats and comparing the most relevant cardiac and vascular parameters at the same HR (75 bpm). Present outcomes evidence that the arterial system is not able to completely absorb the AF-induced variability, which can be even amplified towards the peripheral circulation. AF is also able to locally alter the wave dynamics, by modifying the interplay between forward and backward signals. The sole heart rhythm variation (i.e., from NSR to AF) promotes an alteration of the regular dynamics at the arterial level which, in terms of pressure and peripheral perfusion, suggests a modification of the physiological phenomena ruled by periodicity (e.g., regular organ perfusion)and a possible vascular dysfunction due to the prolonged exposure to irregular and extreme values. The present study represents a first modeling approach to characterize the variability of arterial hemodynamics in presence of AF, which surely deserves further clinical investigation., Comment: 17 pages, 8 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.

Acute inferior myocardial infarction (MI) is often associated with heart blocks as acute occlusion of the right coronary artery (RCA) compromises flow to sinoatrial nodal and atrioventricular nodal branches that arise from the proximal and distal parts of RCA, respectively. This phenomenon can happen in up to 20% of the cases, is usually transient and rarely requires pacemaker implantation. Still, when present, it raises alarms and poses significant monitoring and management challenges to the physicians. We report a similar case where the patient presented with inferior MI complicated by complete heart block. The patient went through varying degrees of advanced heart blocks before finally reverting to normal sinus rhythm. Subsequent coronary angiogram showed total thrombotic occlusion of proximal RCA. To the best of our knowledge, such a case has not been described before in literature where a complete array of heart blocks is reported in the context of acute MI. This case illustrates the importance of recognizing advanced heart blocks in acute MI. Although usually transient, these arrhythmias need careful monitoring during the initial days after MI.

Background: Even though left atrial (LA) size and function are intimately related to left ventricular (LV) diastolic dysfunction, the role of LA with regard to LV systolic function is less clear. Consequently, we examined the potential association that might exist between measures of longitudinal LV systolic shortening and LA dilation using LA volume index (LAVI). Methods: In this retrospective analysis, data from 75 echocardiograms (mean age 53 ± 14; range 24 - 89 years; mean body surface area (BSA) 2.0 ± 0.3) were analyzed. Results: Peak global longitudinal (PGLS) correlated best with LV mass index (LVMI) followed by mitral annular systolic excursion (MAPSE), and age. Similar results were obtained when analyzing the best variables that correlated with LAVI. Finally, MAPSE correlated best with PGLS, then with MA tissue Doppler systolic velocity, BSA, and LAVI in that order. All patients had normal LV ejection fraction (LVEF) and normal sinus rhythm when studied. Conclusions: LAVI does not directly affect LV systolic function and longitudinal measures of LV shortening are mainly dependent on LV mass. Additional studies are now required to determine how these associations vary when different degrees of LV dilatation and systolic dysfunction are included in the analysis. Cardiol Res. 2018;9(1):1-6 doi: https://doi.org/10.14740/cr637w Â

Arrhythmias are abnormal heart rhythms that can be life-threatening. Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Supraventricular Tachycardia (SVT), Sinus Tachycardia (ST), and Sinus Bradycardia (SB) are common arrhythmias that affect a growing number of patients. In this paper we describe a method to detect these arrhythmias in RR interval signals. We propose a deep learning algorithm to discriminate these fife arrhythmias and Normal Sinus Rhythm (NSR). The deep learning model was trained and tested with data from 10093 subjects. We used 10-fold cross-validation to establish the performance results. The overall accuracy for the six-class problem was 98.37%. When considering the binary problem of arrhythmia versus NSR, where the arrhythmia group is formed by combining the data from all fife arrythmias, the performance results are: Accuracy (ACC) = 98.55%, Sensitivity (SEN) = 99.40%, Specificity (SPE) = 94.30%. These results indicate that it is possible to discriminate RR interval sequences from SVT, ST, SB, AFIB, AFL, and NSR subjects with minimal error. Furthermore, the proposed model can provide a robust and independent second opinion when it comes to a decision if arrhythmia is present or not. Another positive aspect of the proposed arrhythmia detection algorithm is economic viability. RR interval signals are cost-effective to measure, communicate, and process. The discriminate powers of the proposed algorithm together with the advent of wearable technology and m-health infrastructure might lead to pervasive long-term arrhythmia monitoring. The detection results can support early diagnosis which helps to reduce the burden of the disease.

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.