Predictors and timeline of spontaneous conversion to normal sinus rhythm: A single center retrospective cohort study of patients with symptomatic atrial fibrillation [PDF]
Introduction: Annual healthcare expenditures associated with atrial fibrillation (AF) in the United States (US) continue to grow as more symptomatic patients present to emergency departments (ED).
Shubash Adhikari +5 more
doaj +4 more sources
Predicting Future Incidences of Cardiac Arrhythmias Using Discrete Heartbeats from Normal Sinus Rhythm ECG Signals via Deep Learning Methods [PDF]
This study aims to compare the effectiveness of using discrete heartbeats versus an entire 12-lead electrocardiogram (ECG) as the input for predicting future occurrences of arrhythmia and atrial fibrillation using deep learning models.
Yehyun Kim +7 more
doaj +2 more sources
A case of successful catheter ablation of blocked atrial bigeminy and bradycardia with the recovery of normal sinus rhythm and myocardial reverse remodeling [PDF]
A 69‐year‐old man presented bradycardia with a constant blocked atrial bigeminy and heart failure. Successful catheter ablation of blocked atrial bigeminy with bradycardia resulted in myocardial reverse remodeling and restoration of the normal sinus ...
Tomomi Sugiyama +5 more
doaj +2 more sources
Conversion of supraventricular tachycardia to normal sinus rhythm by dexmedetomidine treatment [PDF]
Cardiac dysrhythmias are an important cause of morbidity and mortality in the perioperative period, and they are more common after thoracic surgery and are most often supraventricular in origin [1]. Dexmedetomidine, a highly selective α-2 adrenoceptor agonist, is increasingly being used in anesthesia and critical care because it not only produces ...
Cheol Lee +4 more
doaj +3 more sources
Classification of atrial fibrillation and normal sinus rhythm based on convolutional neural network. [PDF]
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 ...
Huang ML, Wu YS.
europepmc +4 more sources
Towards Feasible Home ECG Monitoring: AI-Driven Detection of Clinically Critical Arrhythmias Using Single-Lead Signals [PDF]
Differentiating life-threatening arrhythmias, such as ventricular tachycardia and supraventricular tachycardia, from non-threatening ones is crucial for clinical applications.
Chia-Hsien Hsu +3 more
doaj +2 more sources
Atrial fibrillation (AF) is usually detected by inspection of the electrocardiogram waveform, a task made difficult when the signal is distorted by noise. The RR interval time series is more frequently available and accurate, yet linear and nonlinear time series analyses that detect highly varying and irregular AF are vulnerable to the common finding ...
Marta Carrara, Manuela Ferrario
exaly +3 more sources
Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning [PDF]
Manasi Nandi, Celia M Marr
exaly +2 more sources
Comparison of the Relationship Between Inflammatory Markers and Atrial Fibrillation Burden
Background: Atrial fibrillation is a complex disease with irregular ventricular response and tachycardia as a result of irregular and rapid contraction of the atria, with poor cardiovascular outcomes unless treated. Various mechanisms are involved in its
Sefa Erdi Ömür +2 more
doaj +1 more source
Background: Atrial flutter is a common arrhythmia in structurally normal or abnormal heart. The electrocardiographic features of it can be mistaken for sinus tachycardia or supraventricular tachycardia. By careful electrocardiogram (ECG) inspection or by
Amar T. Alhamdi
doaj +9 more sources

