Cardiovascular

Arrhythmias

Latest AI and machine learning research in arrhythmias for healthcare professionals.

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A CNN Model for Cardiac Arrhythmias Classification Based on Individual ECG Signals.

PURPOSE: Wearable devices in the scenario of connected home healthcare integrated with artificial in...

Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG.

Atrial fibrillation (AF) is the most common supraventricular cardiac arrhythmia, resulting in high m...

Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model - A Cross-Sectional Study.

Atrial fibrillation (AF) is the most common arrhythmia and is associated with increased thromboembo...

Expert-enhanced machine learning for cardiac arrhythmia classification.

We propose a new method for the classification task of distinguishing atrial fibrillation (AFib) fro...

Detection of maternal and fetal stress from the electrocardiogram with self-supervised representation learning.

In the pregnant mother and her fetus, chronic prenatal stress results in entrainment of the fetal he...

Detection of Left Atrial Myopathy Using Artificial Intelligence-Enabled Electrocardiography.

BACKGROUND: Left atrial (LA) myopathy is common in patients with heart failure and preserved ejectio...

Enhancing electrocardiographic analysis by combining a high-resolution 12-lead ECG with novel software tools.

INTRODUCTION: Signal-averaged electrocardiography is a non-invasive, computerized technique that amp...

Deep Learning Techniques in the Classification of ECG Signals Using R-Peak Detection Based on the PTB-XL Dataset.

Deep Neural Networks (DNNs) are state-of-the-art machine learning algorithms, the application of whi...

Real-Time Arrhythmia Detection Using Hybrid Convolutional Neural Networks.

Background Accurate detection of arrhythmic events in the intensive care units (ICU) is of paramount...

Mental Stress Classification Based on a Support Vector Machine and Naive Bayes Using Electrocardiogram Signals.

Examining mental health is crucial for preventing mental illnesses such as depression. This study pr...

Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis.

Horizon scanning for innovative technologies that might be applied to medical products and requires ...

Inter-patient automated arrhythmia classification: A new approach of weight capsule and sequence to sequence combination.

OBJECTIVE: We propose a new capsule network to compensate for the information loss in the deep convo...

Evolution of single-lead ECG for STEMI detection using a deep learning approach.

BACKGROUND: While ST-Elevation Myocardial Infarction (STEMI) door-to-balloon times are often below 9...

Automatic Multi-Label ECG Classification with Category Imbalance and Cost-Sensitive Thresholding.

Automatic electrocardiogram (ECG) classification is a promising technology for the early screening a...

Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems.

BACKGROUND AND OBJECTIVES: Most deep-learning-related methodologies for electrocardiogram (ECG) clas...

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

Recent global developments underscore the prominent role big data have in modern medical science. Bu...

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation.

BACKGROUND: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient e...

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