Cardiovascular

Arrhythmias

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

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Showing 169-189 of 1,699 articles
A deep learning model for QRS delineation in organized rhythms during in-hospital cardiac arrest.

BACKGROUND: Cardiac arrest (CA) is the sudden cessation of heart function, typically resulting in lo...

Improving myocardial infarction diagnosis with Siamese network-based ECG analysis.

BACKGROUND: Heart muscle damage from myocardial infarction (MI) is brought on by insufficient blood ...

tinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosign...

A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy.

INTRODUCTION: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in younge...

Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a h...

Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia...

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis pat...

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the c...

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

OBJECTIVE: In Long-Term Monitoring (LTM), noise significantly impacts the quality of the electrocard...

A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors.

In recent years, machine learning has gained substantial attention for its ability to predict comple...

Residual-attention deep learning model for atrial fibrillation detection from Holter recordings.

BACKGROUND: Detecting subtle patterns of atrial fibrillation (AF) and irregularities in Holter recor...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiologi...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using...

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks.

This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that bala...

Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.

We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardi...

Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardi...

Application of machine learning in predicting postoperative arrhythmia following transcatheter closure of perimembranous ventricular septal defects.

BACKGROUND: Arrhythmia is a frequent complication following transcatheter device closure of perimemb...

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.

BACKGROUND: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosi...

MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that ...

Predicting lack of clinical improvement following varicose vein ablation using machine learning.

OBJECTIVE: Varicose vein ablation is generally indicated in patients with active/healed venous ulcer...

12 lead surface ECGs as a surrogate of atrial electrical remodeling - a deep learning based approach.

BACKGROUND AND PURPOSE: Atrial fibrillation (AF), a common arrhythmia, is linked with atrial electri...

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