Latest AI and machine learning research in arrhythmias for healthcare professionals.
BACKGROUND: Out-of-hospital cardiac arrest (OHCA) survival in China remains critically low due to li...
Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people glo...
Artificial intelligence (AI) is transforming the role of electrocardiography (ECG) in cardiovascular...
Electrocardiogram (ECG) signals are significantly distorted during recording by muscle artifact (MA)...
PURPOSE: The long-term objective of the Ablation-IMaging and Advanced Guidance for workflow optimiza...
Electronic health records, biobanks, and wearable biosensors enable the collection of multiple healt...
Arrhythmia is a prevalent cardiac disorder that can lead to severe complications such as stroke and ...
BACKGROUND: A wrist-worn wearable device for acquiring limb and chest ECG leads (wECG) may constitut...
PURPOSE: Electrocardiogram (ECG)-gated cine imaging in breath-hold enables high-quality diagnostics ...
L-loop congenitally corrected transposition of the great arteries (ccTGA) is a rare congenital heart...
Atrial fibrillation is a prevalent cardiac arrhythmia, significantly increasing the risk of stroke, ...
Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia, and it is associated with substan...
Image-guided minimally invasive ultrasound thermal ablation has been widely studied for disease trea...
Coronary microvascular disease (CMD), particularly prevalent among women, is associated with increas...
This research paper presents a systematic approach to ECG beat classification using advanced machine...
Atrial fibrillation (Afib) recurrence following catheter ablation (CA) remains a significant challen...
The inherited arrhythmia (IA) syndromes are a group of rare and complex conditions that may predispo...
Sleep apnea is a prevalent chronic disorder posing significant health risks, requiring prompt diagno...
OBJECTIVES: Most patients presenting with chest pain in the emergency medical services (EMS) setting...
This study aims to enhance the accuracy and efficiency of energy consumption prediction during exerc...
OBJECTIVE: We present the first multimodal deep learning framework combining ultrasound (US) and ele...