Cardiovascular

Arrhythmias

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

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An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.

Diagnosing COVID-19 accurately and rapidly is vital to control its quick spread, lessen lockdown res...

A deep learning approach identifies new ECG features in congenital long QT syndrome.

BACKGROUND: Congenital long QT syndrome (LQTS) is a rare heart disease caused by various underlying ...

Explainable detection of myocardial infarction using deep learning models with Grad-CAM technique on ECG signals.

Myocardial infarction (MI) accounts for a high number of deaths globally. In acute MI, accurate elec...

Automatic Evaluation of Motor Rehabilitation Exercises Based on Deep Mixture Density Neural Networks.

An automatic assessment system for physical telerehabilitation could reduce the time and cost of tre...

A Meta-Learning Approach for Fast Personalization of Modality Translation Models in Wearable Physiological Sensing.

Modality translation grants diagnostic value to wearable devices by translating signals collected fr...

Effects of data and entity ablation on multitask learning models for biomedical entity recognition.

MOTIVATION: Training domain-specific named entity recognition (NER) models requires high quality han...

Development of a Visualization Deep Learning Model for Classifying Origins of Ventricular Arrhythmias.

BACKGROUND: Several algorithms have been proposed for differentiating the right and left outflow tra...

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset.

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines p...

Machine learning-based heart disease diagnosis: A systematic literature review.

Heart disease is one of the significant challenges in today's world and one of the leading causes of...

Premature Ventricular Contraction Recognition Based on a Deep Learning Approach.

Electrocardiogram signal (ECG) is considered a significant biological signal employed to diagnose he...

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification.

BACKGROUND AND OBJECTIVE: The automatic recognition of myocardial infarction (MI) by artificial inte...

Deep Learning-Based Electrocardiograph in Evaluating Radiofrequency Ablation for Rapid Arrhythmia.

This study is aimed at analyzing the important role of deep learning-based electrocardiograph (ECG) ...

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may caus...

Automatic detection of arrhythmias from an ECG signal using an auto-encoder and SVM classifier.

Millions of people around the world are affected by arrhythmias, which are abnormal activities of th...

Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion.

PURPOSE: Surface electromyography (sEMG) is vulnerable to environmental interference, low recognitio...

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG.

OBJECTIVES: Sleep apnea is the most common sleep disorder that leads to serious health complications...

Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.

Physicians manually interpret an electrocardiogram (ECG) signal morphology in routine clinical pract...

MCG-Net: End-to-End Fine-Grained Delineation and Diagnostic Classification of Cardiac Events From Magnetocardiographs.

In this paper, we propose an end-to-end deep learning architecture, referred as MCG-Net, integrating...

Update on risk factors and biomarkers of sudden unexplained cardiac death.

Sudden cardiac death (SCD) accounts for approximately 15%-20% of all deaths worldwide, the causes of...

Deep learning for predicting respiratory rate from biosignals.

In the past decade, deep learning models have been applied to bio-sensors used in a body sensor netw...

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