Cardiovascular

Arrhythmias

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

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Hyperglycemia Identification Using ECG in Deep Learning Era.

A growing number of smart wearable biosensors are operating in the medical IoT environment and those...

ECG data dependency for atrial fibrillation detection based on residual networks.

Atrial fibrillation (AF) is an arrhythmia that can cause blood clot and may lead to stroke and heart...

Machine Learning for Real-Time Heart Disease Prediction.

Heart-related anomalies are among the most common causes of death worldwide. Patients are often asym...

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to...

A Deep Learning-Enabled Electrocardiogram Model for the Identification of a Rare Inherited Arrhythmia: Brugada Syndrome.

BACKGROUND: Brugada syndrome is a major cause of sudden cardiac death in young people and has distin...

LwF-ECG: Learning-without-forgetting approach for electrocardiogram heartbeat classification based on memory with task selector.

Most existing Electrocardiogram (ECG) classification methods assume that all arrhythmia classes are ...

Validation of a Whole Heart Segmentation from Computed Tomography Imaging Using a Deep-Learning Approach.

The aim of this study is to develop an automated deep-learning-based whole heart segmentation of ECG...

Deep neural network-estimated electrocardiographic age as a mortality predictor.

The electrocardiogram (ECG) is the most commonly used exam for the evaluation of cardiovascular dise...

Detection of hypertrophic cardiomyopathy by an artificial intelligence electrocardiogram in children and adolescents.

BACKGROUND: There is no established screening approach for hypertrophic cardiomyopathy (HCM). We rec...

Lead Reconstruction Using Artificial Neural Networks for Ambulatory ECG Acquisition.

One of the most powerful techniques to diagnose cardiovascular diseases is to analyze the electrocar...

Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study.

Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disea...

Deep learning methods for screening patients' S-ICD implantation eligibility.

Subcutaneous Implantable Cardioverter-Defibrillators (S-ICDs) are used for prevention of sudden card...

Machine Learning for personalised stress detection: Inter-individual variability of EEG-ECG markers for acute-stress response.

Stress appears as a response for a broad variety of physiological stimuli. It does vary among indivi...

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorith...

Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging.

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is...

Artificial Intelligence-Enabled Electrocardiography to Screen Patients with Dilated Cardiomyopathy.

Undiagnosed dilated cardiomyopathy (DC) can be asymptomatic or present as sudden cardiac death, ther...

Electrocardiogram-Based Emotion Recognition Systems and Their Applications in Healthcare-A Review.

Affective computing is a field of study that integrates human affects and emotions with artificial i...

Artificial Intelligence-Enabled ECG to Identify Silent Atrial Fibrillation in Embolic Stroke of Unknown Source.

OBJECTIVES: Embolic strokes of unknown source (ESUS) are common and often suspected to be caused by ...

Practical fine-grained learning based anomaly classification for ECG image.

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for asse...

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