Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Showing 715-735 of 6,892 articles
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...

A Machine Learning Approach to Predict Acute Ischemic Stroke Thrombectomy Reperfusion using Discriminative MR Image Features.

Mechanical thrombectomy (MTB) is one of the two standard treatment options for Acute Ischemic Stroke...

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...

Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach.

BACKGROUND: Conventional risk score for predicting short and long-term mortality following an ST-seg...

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...

xECGNet: Fine-tuning attention map within convolutional neural network to improve detection and explainability of concurrent cardiac arrhythmias.

Background and objectiveDetecting abnormal patterns within an electrocardiogram (ECG) is crucial for...

MEGnet: Automatic ICA-based artifact removal for MEG using spatiotemporal convolutional neural networks.

Magnetoencephalography (MEG) is a functional neuroimaging tool that records the magnetic fields indu...

ECG quality assessment based on hand-crafted statistics and deep-learned S-transform spectrogram features.

Background and Objective Electrocardiogram (ECG) quality assessment is significant for automatic dia...

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