Latest AI and machine learning research in arrhythmias for healthcare professionals.
Detecting and classifying arrhythmias is essential in diagnosing cardiovascular diseases. However, current deep learning-based classification methods often encounter difficulties in effectively integrating both the morphological and temporal features of Electrocardiograms (ECGs). To address this challenge, we propose a Convolutional Neural Network (CNN) that incorporates mixed scales and hierarchi...
Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these effects can be anticipated by monitoring the dynamic changes in vital signs and other physiological signals or parameters such as heart rate, blood pressure (BP), Electrocardiogram (ECG), and Photoplethysmogram (PPG), which subtly encode smoking-related...
Understanding the intricate relationship between sleep quality and cardiovascular outcomes opens new avenues for risk stratification in cardiovascula...
OBJECTIVES: This study aimed to validate the agreement and diagnostic performance of a deep-learning-based coronary artery calcium scoring (DL-CACS) s...
BACKGROUND: Artificial intelligence (AI) has shown potential in enhancing ECG analysis, but its accuracy in detecting atrial fibrillation (AF) in COVI...
Atrial fibrillation (AF) is increasingly recognized as the clinical manifestation of an underlying atrial disease process rather than a purely electri...
Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have involved the analysis of various modalities, including...
The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (R...
Electrocardiogram (ECG) classification is crucial for automated cardiac disease diagnosis, yet existing methods often struggle to capture local morp...
The safety and efficacy of irreversible electroporation (IRE) in tumor therapy has been validated over many years by clinical application. An in-depth...
AIMS: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural opt...
The dynamic nature of human health and comfort calls for adaptive systems that respond to individual physiological needs in real time. This paper pr...
Virtual studies of ICD behaviour are crucial for testing device functionality in a controlled environment prior to clinical application. Although pr...
This narrative review addresses the diagnostic complexities of vasospastic angina (VSA), also known as Prinzmetal angina, by analyzing findings from p...
Non-compaction cardiomyopathy (NCCM) is a rare, congenital form of cardiomyopathy characterized by excessive trabeculations in the left ventricle myoc...
Cardiac arrhythmias are a leading cause of life-threatening cardiac events, highlighting the urgent need for accurate and timely detection. Electroc...
Leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for analyzing medical data, particularly Electrocardiogram (ECG), ...
Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with severe complications such as ischemic stroke and heart failure. Early detec...
Electrocardiogram (ECG) signals are frequently corrupted by noise, such as baseline wander (BW), muscle artifacts (MA), and electrode motion (EM), w...
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, highlighting the critical need for efficient and accurate diagnostic...