Supraventricular ectopic beats

The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm overlook the probability distribution differences between the source domain (SD) and target domain (TD) databases. The motivation of this paper is to address the challenge of labeled data scarcity at the model level while exploring an effective method to eliminate domain discrepancy between SD and TD databases, especially when SD and TD are derived from inconsistent tasks...

Building an electrocardiogram (ECG) heartbeat classification model is essential for early arrhythmia detection. This research aims to build a reliable model that can classify heartbeats into five heartbeat types: normal beat (N), supraventricular ectopic beat (SVEB), ventricular ectopic beat (VEB), fusion beat (F), and unknown beat (Q), with a focus on enhancing the predictions of the uncommon Q and F heartbeats. The base dataset used is the MIT-BIH SupraVentricular Database, which was used to train and compare the performance of five machine learning models: logistic regression, Random Forest (RF), K-nearest neighbor, linear support vector machine, and linear discriminant analysis...

INTRODUCTION/OBJECTIVES: The aim of this study was to compare a novel small event recorder device, the Carnation Ambulatory Monitor (CAM), with a standard Holter. ANIMALS: Nineteen adult dogs. MATERIAL AND METHODS: Comparative and explorative study. The two devices were simultaneously applied for approximately 24 h. RESULTS: analysis time (p=0.013) and percentage of artefacts (p<0.001) were greater for the CAM (110 min [40-264]; and 9% [0-34], respectively) compared to a standard Holter (30 min [18-270]; and 0...

Electrocardiogram (ECG) is a non-invasive medical tool that divulges the rhythm and function of the human heart. This is broadly employed in heart disease detection including arrhythmia. Arrhythmia is a general term for abnormal heart rhythms that can be identified and classified into many categories. Automatic ECG analysis is provided by arrhythmia categorization in cardiac patient monitoring systems. It aids cardiologists to diagnose the ECG signal. In this work, an Ensemble classifier is proposed for accurate arrhythmia detection using ECG Signal...

The well-defined concept of aberrant ventricular conduction was introduced over 100 years ago and, despite advances in cardiac physiology and electrophysiologic testing, it is still widely misunderstood. Aberrant ventricular conduction is due to physiologic refractoriness of the His-Purkinje system and in most cases does not reflect underlying conduction system disease. Electrophysiologically, aberrant ventricular conduction can manifest with premature atrial ectopics, the Ashman phenomenon with atrial tachyarrhythmias, concealed conduction, echo beats and with the sinus mechanism including rate dependent bundle branch block, bradycardia dependent bundle branch block and early sinus beats...

BACKGROUND: Total abnormal pulmonary venous return anomaly is a CHD characterised by abnormal pulmonary venous flow directed to the right atrium. In this study, we aimed to compare the effects of these techniques on early rhythm problems in total abnormal pulmonary venous return anomaly cases operated with conventional or primary sutureless techniques. METHOD: Seventy consecutive cases (median age 1 month, median weight 4 kg) who underwent total abnormal pulmonary venous return anomaly repair with conventional or primary sutureless technique between May 1 2020 and May 1 2022 were evaluated...

Cardiac arrhythmia is an abnormal rhythm of the heartbeat and can be life-threatening Electrocardiogram (ECG) is a technology that uses an electrocardiograph machine to record a graph of the changes in electrical activity produced by the heart at each cardiac cycle. ECG can generally be used to check whether the examinee has arrhythmia, ion channel disease, cardiomyopathy, electrolyte disorder and other diseases. To reduce the workload of doctors and improve the accuracy of ECG signal recognition, a novel and lightweight automatic ECG classification method based on Convolutional Neural Network (CNN) is proposed...

BACKGROUND AND PURPOSE: Although supraventricular ectopic beats (SVE), including premature atrial contractions (PACs) and non-sustained atrial tachycardia (NSAT), are frequent in the general population, some study results indicate that they are pathologic. SVE may predict undiagnosed atrial fibrillation or be associated with the embolic pattern of ischemic stroke. The aim of this study was to identify the indicators most associated with embolic stroke among the parameters that suggest the burden of SVE...

Wilson assumed that the ventricular gradient (VG) is independent of the ventricular activation order. This paradigm has often been refuted and was never convincingly corroborated. We sought to validate Wilson's concept by intra-individual comparison of the VG of sinus beats and ectopic beats, thus assessing the effects of both altered ventricular conduction (caused by the ectopic focus) and restitution (caused by ectopic prematurity). We studied standard diagnostic ECGs of 118 patients with accidental extrasystoles: normally conducted supraventricular ectopic beats (SN, N = 6) and aberrantly conducted supraventricular ectopic beats (SA, N = 20) or ventricular ectopic beats (V, N = 92)...

Automated electrocardiogram (ECG) classification using machine learning (ML) is extensively utilized for arrhythmia detection. Contemporary ML algorithms are typically deployed on the cloud, which may not always meet the availability and privacy requirements of ECG monitoring. Edge inference is an emerging alternative that overcomes the concerns of cloud inference; however, it poses new challenges due to the demanding computational requirements of modern ML algorithms and the tight constraints of edge devices...

BACKGROUND AND OBJECTIVE: Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs...

BACKGROUND: Incident atrial fibrillation (AF) occurs in 5-10% of patients after acute myocardial infarction (AMI) and is associated with adverse outcomes. Guidelines now recommend screening for AF in all elderly patients. However, the relevance of screen-detected AF and short episodes of irregular supraventricular ectopic beats ('micro-AF') after AMI is unknown. OBJECTIVES: To investigate the value of two-week intermittent ECG screening to detect incident AF and 'micro-AF' in elderly patients 12 months after an AMI, and its association with risk of cardiovascular events...

BACKGROUND: The prevalence and clinical importance of cardiac abnormalities in dogs with acute pancreatitis (AP) is unknown. ANIMALS: Twelve dogs with AP and 60 archived serum samples from dogs with suspected AP. METHODS: Two-phase study. PHASE I: Analysis of archived serum samples from dogs with clinical signs of AP and high Spec cPL concentrations. High sensitivity troponin I (TnIH) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) concentrations were measured in achieved serum samples...

Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart's rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors' workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St...

Paroxysmal atrioventricular block (PAVB) is characterized by a sudden and unanticipated repetitive block of atrial impulses to the ventricles. It is often triggered by supraventricular and ventricular ectopic beats in patients with diseased His-Purkinje system. We present the case of a 69-year-old woman with a history of fascicular block who was admitted with gastrointestinal bleeding. Her hospital course was complicated by cardiac arrest. At the time of the loss of consciousness, telemetry tracings showed sudden onset high-grade second-degree atrioventricular (AV) block with a delayed escape rhythm resulting in a prolonged pause...

CONTEXT: During the COVID-19 pandemic, essential in-person electrocardiogram (ECG) recordings became unfeasible, while patients continued to suffer from cardiac conditions. To circumvent these challenges, the cardiology clinic (Long Island Heart Rhythm Center [LIHRC]) at the New York Institute of Technology College of Osteopathic Medicine (NYITCOM) transitioned to a remote real-time outpatient cardiac telemetry (ROCT) service. OBJECTIVES: The goal of this study is to test the hypothesis that at-home ROCT, provided by the LIHRC, is an effective method of providing ECG monitoring to symptomatic patients during the COVID-19 pandemic...

Atrial fibrillation (AF) is an irregular heart rhythm, characterised by chaotic atrial activation, which is promoted by remodelling. Once initiated, AF can also propagate the progression of itself in the so-called ''AF begets AF''. Several lines of investigation have shown that signalling molecules, including reactive oxygen species, angiotensin II, and phosphoinositide 3-kinases (PI3Ks), in presence or absence of cardiovascular disease risk factors, stabilise and promote AF maintenance. In particular, reduced cardiac-specific PI3K activity that is not associated with oncology is cardiotoxic and increases susceptibility to AF...

BACKGROUND: Phase I KAT301 trial investigated the use of intramyocardial adenoviral vascular endothelial growth factor -DΔNΔC (AdVEGF-D) gene therapy (GT) to alleviate symptoms in refractory angina (RA) patients. In KAT301, 30 patients with RA were randomized to AdVEGF-D or control group in 4:1 ratio. The treatment was found feasible, increased myocardial perfusion, and reduced angina symptoms at 1-year follow-up. However, there is some evidence suggesting that intramyocardial delivery route and overexpression of VEGFs might induce ventricular arrhythmias...

Cardiac arrhythmia is a leading cause of cardiovascular disease, with a high fatality rate worldwide. The timely diagnosis of cardiac arrhythmias, determined by irregular and fast heart rate, may help lower the risk of strokes. Electrocardiogram signals have been widely used to identify arrhythmias due to their non-invasive approach. However, the manual process is error-prone and time-consuming. A better alternative is to utilize deep learning models for early automatic identification of cardiac arrhythmia, thereby enhancing diagnosis and treatment...

Beat-by-beat arrhythmia detection in ambulatory electrocardiogram (ECG) monitoring is critical for the evaluation and prognosis of cardiac arrhythmias, however, it is a highly professional demanding and time-consuming task. Current methods for automatic beat-by-beat arrhythmia detection suffer from poor generalization ability due to the lack of large-sample and finely-annotated (labels are given to each beat) ECG data for model training. In this work, we propose a weakly supervised deep learning framework for arrhythmia detection (WSDL-AD), which permits training a fine-grained (beat-by-beat) arrhythmia detector with the use of large amounts of coarsely annotated ECG data (labels are given to each recording) to improve the generalization ability...