Arrhythmias

Energy consumption analysis and prediction in exercise training based on accelerometer sensors and deep learning.

This study aims to enhance the accuracy and efficiency of energy consumption prediction during exercise training and address the limitations of existing methods in terms of data feature extraction, model complexity, and adaptability to practical applications. This study proposes an optimized energy consumption prediction model based on accelerometer sensor data and deep learning techniques. In thi...

Phantom-Based Ultrasound-ECG Deep Learning Framework for Prospective Cardiac Computed Tomography.

OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and electrocardiography (ECG) data to predict cardiac quiescent periods (QPs) for optimized computed tomography angiography gating (CTA).

Predicting the Higher Energy Need for Effective Defibrillation Using Machine Learning Based on an Animal Model.

: Early defibrillation improves outcomes in cardiac arrest, but the optimal defibrillation strategy and energy requirements remain debated. This study...

Cryo-ablation management of atrial fibrillation in Fabry disease without agalsidase alpha: a case report.

Fabry disease (FD) is a rare genetic disorder caused by mutations in the gene, affecting multiple organs. Over 60% of patients experience heart-relat...

A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram.

BACKGROUND: Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The object...

Brugada syndrome.

Brugada syndrome (BrS) is a cardiac channelopathy associated with an elevated risk of arrhythmias and sudden cardiac death compared with the general p...

Determinants of artificial intelligence electrocardiogram-derived age and its association with cardiovascular events and mortality: a systematic review and meta-analysis.

Artificial intelligence (AI)-ECG-derived age (AI-ECG age) and Heart Delta Age (HDA)-the difference between AI-ECG and chronological age-are emerging t...

Complex Fractionated Atrial Electrograms, Dispersion, and Voltage in Persistent Atrial Fibrillation: Implications for Electrogram-Guided Ablation.

BACKGROUND: Various methods have been used to identify substrate of persistent atrial fibrillation (PeAF) including complex fractionated atrial electr...

Knowledge-Driven Graph Representation Learning for Myocardial Infarction Localization.

The electrocardiogram (ECG) serves as a crucial tool for myocardial infarction (MI) localization, and deep learning methods have proven effective in a...

MicroRNA signature predicts post operative atrial fibrillation after coronary artery bypass grafting.

Early detection of atrial fibrillation (AFib) is crucial for altering its natural progression and complication profile. Traditional demographic and li...

High burden of premature ventricular contractions upregulates transcriptional markers of inflammation and promotes adverse cardiac remodeling linked to cardiomyopathy.

Premature ventricular contractions (PVCs) are the most prevalent ventricular arrhythmia in adults. High PVC burden can lead to left ventricular (LV) s...

ECG Screening in Athletes: A Systematic Review of Sport, Age, and Gender Variations.

BACKGROUND: The electrocardiogram (ECG) screening in athletes is essential due to the unique cardiac adaptations induced by intensive training. Howeve...

Cardiac CT for electrophysiological interventions.

Cardiac computed tomography (CCT) holds an important role in the field of electrophysiology offering critical insights that enhance the management of ...

Neuronal network analysis during short time transcranial direct current stimulation with permanent EEG measurement in early childhood: a feasibility study.

Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in...

ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction.

OBJECTIVE: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart...

A pediatric ECG database with disease diagnosis covering 11643 children.

Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to a...

ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks.

Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of t...

Advancements in Biomarkers for Early Detection and Risk Stratification of Cardiovascular Diseases-A Literature Review.

INTRODUCTION: CVDs is a leading cause of morbidity, mortality, and healthcare expenditure worldwide. Identifying individuals at risk or in the incipie...

Predicting Mortality in Atrial Fibrillation Patients Treated with Direct Oral Anticoagulants: A Machine Learning Study Based on the MIMIC-IV Database.

Atrial fibrillation (AF) is a common arrhythmia linked to increased mortality and significant healthcare burden, especially in the elderly. Direct Or...

Development of a Heart Rate Variability Based Ambulatory Stress Detection Model for Clinical Populations.

Biosensor-based, real-time stress detection has generated clinical interest for the purpose of driving just-in-time interventions that support recover...