Cardiovascular

Arrhythmias

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

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Robotic-assisted thermal ablation of liver tumours.

OBJECTIVE: This study aimed to assess the technical success, radiation dose, safety and performance ...

An ontology-based annotation of cardiac implantable electronic devices to detect therapy changes in a national registry.

The patient population benefitting from cardiac implantable electronic devices (CIEDs) is increasing...

An Electrocardiogram Multi-Task Benchmark with Comprehensive Evaluations and Insightful Findings.

In the process of patient diagnosis, non-invasive measurements are widely used due to their low risk...

Predicting Diabetes Using Convolutional Neural Networks and EKG Entropy Analysis.

Heart Rate Variability (HRV) is associated with diabetic complications. This analysis can quantify c...

Optimizing the Primary Prevention of Sudden Cardiac Death in Patients With Heart Failure.

Implantable cardioverter-defibrillators (ICDs) protect patients from sudden cardiac death (SCD). Lan...

A hybrid approach for machine learning based beat classification of ECG using different digital differentiators and DTCWT.

This research paper presents a systematic approach to ECG beat classification using advanced machine...

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

This study aims to introduce a novel, computationally lightweight feature extraction technique calle...

A hybrid machine learning approach using particle swarm optimization for cardiac arrhythmia classification.

BACKGROUND: Precise and rapid identification of cardiac arrhythmias is paramount for delivering opti...

State-of-the-art analysis of electrocardiogram findings in sudden cardiac death.

Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have invo...

Self-supervised learning for low-dose CT image denoising method based on guided image filtering.

low-dose computed tomography (LDCT) images suffer from severe noise due to reduced radiation exposur...

Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.

BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identifica...

Traditional Chinese Medicine for Anti-Arrhythmias: Mechanisms via Potassium Channels.

Cardiac arrhythmia is a common life-threatening cardiovascular disorder. Potassium channels play a c...

Turbulence control in memristive neural network via adaptive magnetic flux based on DLS-ADMM technique.

High-voltage defibrillation for eliminating cardiac spiral waves has significant side effects, neces...

An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.

The rapid evolution of artificial intelligence (AI), together with the increased availability of soc...

Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would...

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate....

Full duty cycle laser ablation plasma spectrometry (LAPS) combining a hollow-core toroidal coil with artificial intelligence classification.

The use of a hollow-core toroidal coil (HTC) as an induction detector allows for the analysis of ion...

QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.

We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardi...

Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis.

Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underd...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing condition...

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