Arrhythmias

Hypervascular Nodules and Stiffer Liver are Associated with Recurrence after Microwave Ablation in Patients with Hepatocellular Carcinoma: A Double-Center Analysis.

 The aim of this study was to detect the most important risk factors for recurrence after microwave...

Assessment of Collaborative Robot (Cobot)-Assisted Histotripsy for Venous Clot Ablation.

OBJECTIVE: The application of bubble-based ablation with the focus ultrasound therapy histotripsy is...

Hybrid Prediction Method for ECG Signals Based on VMD, PSR, and RBF Neural Network.

To explore a method to predict ECG signals in body area networks (BANs), we propose a hybrid predict...

A new machine learning approach for predicting likelihood of recurrence following ablation for atrial fibrillation from CT.

OBJECTIVE: To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) pati...

Machine Learning Algorithms for Prediction of Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement.

Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Background Because chest compressions induce artifacts in the ECG, current automated external defibr...

Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis.

Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of ca...

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning.

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased....

Machine Learning in Arrhythmia and Electrophysiology.

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is a...

Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke.

BACKGROUND: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it go...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intrav...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients wit...

Analysis of the nonperfused volume ratio of adenomyosis from MRI images based on fewshot learning.

The nonperfused volume (NPV) ratio is the key to the success of high intensity focused ultrasound (H...

An ECG Signal Classification Method Based on Dilated Causal Convolution.

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. A...

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and sta...

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetic...

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering fr...

Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.

BACKGROUND: QTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, es...

Machine learning method for predicting pacemaker implantation following transcatheter aortic valve replacement.

BACKGROUND: An accurate assessment of permanent pacemaker implantation (PPI) risk following transcat...

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management.

The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload...

Cardiac Severity Classification Using Pre Trained Neural Networks.

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of...