Atrial septum fat deposition and atrial anatomy assessed by cardiac magnetic resonance: relationship to atrial electrophysiology

To assess the prevalence of fat deposition in the atrial septum with and its relationship with 12-lead electrocardiogram (ECG) atrial parameters (PR interval, P wave duration) and the presence of atrial fibrillation

Pre-participation Cardiac Screening in Young Athletes: Models and Criteria

This is the second of two review articles focusing on the value of preparticipation cardiac screening in young athletes. The article focuses on the efficacy of the resting 12-lead electrocardiogram (ECG), physical examination, and medical history questionnaire, which commonly make up the first stage of a cardiac screening protocol. The review then focuses on specific structural and electrical abnormalities which are responsible for sudden cardiac death (SCD) in young athletes – the most common of which is hypertrophic cardiomyopathy. The identification of appropriate ‘red flag’ signs and symptoms is essential for teasing out potential pathological conditions and allowing differentiation from often benign physiological adaptations. The final section provides guidance on how the resting 12-lead ECG can be used to separate pathological from physiological adaptations in young athletes

A 12-lead Electrocardiogram Database for Arrhythmia Research Covering More Than 10,000 Patients

This newly inaugurated research database for 12-lead electrocardiogram signals was created under the auspices of Chapman University and Shaoxing People’s Hospital (Shaoxing Hospital Zhejiang University School of Medicine) and aims to enable the scientific community in conducting new studies on arrhythmia and other cardiovascular conditions. Certain types of arrhythmias, such as atrial fibrillation, have a pronounced negative impact on public health, quality of life, and medical expenditures. As a non-invasive test, long term ECG monitoring is a major and vital diagnostic tool for detecting these conditions. This practice, however, generates large amounts of data, the analysis of which requires considerable time and effort by human experts. Advancement of modern machine learning and statistical tools can be trained on high quality, large data to achieve exceptional levels of automated diagnostic accuracy. Thus, we collected and disseminated this novel database that contains 12-lead ECGs of 10,646 patients with a 500 Hz sampling rate that features 11 common rhythms and 67 additional cardiovascular conditions, all labeled by professional experts. The dataset consists of 10-second, 12-dimension ECGs and labels for rhythms and other conditions for each subject. The dataset can be used to design, compare, and fine-tune new and classical statistical and machine learning techniques in studies focused on arrhythmia and other cardiovascular conditions

What is the best approach to the evaluation of resting tachycardia for an adult?

The best evidence about the diagnostic evaluation of resting tachycardias in adults is currently outlined by practice guidelines. 1 Initial evaluation includes clinical history, physical examination, and 12-lead electrocardiogram (ECG). If the initial evaluation suggests a sinus tachycardia with narrow QRS complexes and no identifiable secondary cause, a 24-hour Holter monitor is usually recommended (strength of recommendation: C, based on expert opinion)

Hybrid machine learning to localize atrial flutter substrates using the surface 12-lead electrocardiogram

Aims Atrial flutter (AFlut) is a common re-entrant atrial tachycardia driven by self-sustainable mechanisms that cause excitations to propagate along pathways different from sinus rhythm. Intra-cardiac electrophysiological mapping and catheter ablation are often performed without detailed prior knowledge of the mechanism perpetuating AFlut, likely prolonging the procedure time of these invasive interventions. We sought to discriminate the AFlut location [cavotricuspid isthmus-dependent (CTI), peri-mitral, and other left atrium (LA) AFlut classes] with a machine learning-based algorithm using only the non-invasive signals from the 12-lead electrocardiogram (ECG). Methods and results Hybrid 12-lead ECG dataset of 1769 signals was used (1424 in silico ECGs, and 345 clinical ECGs from 115 patients—three different ECG segments over time were extracted from each patient corresponding to single AFlut cycles). Seventy-seven features were extracted. A decision tree classifier with a hold-out classification approach was trained, validated, and tested on the dataset randomly split after selecting the most informative features. The clinical test set comprised 38 patients (114 clinical ECGs). The classifier yielded 76.3% accuracy on the clinical test set with a sensitivity of 89.7%, 75.0%, and 64.1% and a positive predictive value of 71.4%, 75.0%, and 86.2% for CTI, peri-mitral, and other LA class, respectively. Considering majority vote of the three segments taken from each patient, the CTI class was correctly classified at 92%. Conclusion Our results show that a machine learning classifier relying only on non-invasive signals can potentially identify the location of AFlut mechanisms. This method could aid in planning and tailoring patient-specific AFlut treatments