Cardiovascular

Myocardial Infarction

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

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Showing 778-798 of 6,892 articles
Deep Neural Network Approach for Continuous ECG-Based Automated External Defibrillator Shock Advisory System During Cardiopulmonary Resuscitation.

Background Because chest compressions induce artifacts in the ECG, current automated external defibr...

Identifying Heart Failure in ECG Data With Artificial Intelligence-A Meta-Analysis.

Electrocardiography (ECG) is a quick and easily accessible method for diagnosis and screening of ca...

Efficiently Updating ECG-Based Biometric Authentication Based on Incremental Learning.

Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased....

Assessment of Thoracic Pain Using Machine Learning: A Case Study from Baja California, Mexico.

Thoracic pain is a shared symptom among gastrointestinal diseases, muscle pain, emotional disorders,...

A deep learning methodology for the automated detection of end-diastolic frames in intravascular ultrasound images.

Coronary luminal dimensions change during the cardiac cycle. However, contemporary volumetric intrav...

A method to screen left ventricular dysfunction through ECG based on convolutional neural network.

OBJECTIVE: This study aims to develop an artificial intelligence-based method to screen patients wit...

Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has caused health concerns worldwide si...

An ECG Signal Classification Method Based on Dilated Causal Convolution.

The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. A...

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.

The application of artificial intelligence (AI) to the electrocardiogram (ECG), a ubiquitous and sta...

Artificial Intelligence-Enabled Assessment of the Heart Rate Corrected QT Interval Using a Mobile Electrocardiogram Device.

BACKGROUND: Heart rate-corrected QT interval (QTc) prolongation, whether secondary to drugs, genetic...

A Hybrid Deep CNN Model for Abnormal Arrhythmia Detection Based on Cardiac ECG Signal.

Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering fr...

Extracting Angina Symptoms from Clinical Notes Using Pre-Trained Transformer Architectures.

Anginal symptoms can connote increased cardiac risk and a need for change in cardiovascular manageme...

Artificial-Intelligence-Enhanced Mobile System for Cardiovascular Health Management.

The number of patients with cardiovascular diseases is rapidly increasing in the world. The workload...

Cardiac Severity Classification Using Pre Trained Neural Networks.

Electrocardiogram (ECG) is the most effective instrument for making decisions about various forms of...

A Review on the State of the Art in Atrial Fibrillation Detection Enabled by Machine Learning.

Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main c...

In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department.

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detec...

Vascular Aging Detected by Peripheral Endothelial Dysfunction Is Associated With ECG-Derived Physiological Aging.

Background An artificial intelligence algorithm that detects age using the 12-lead ECG has been sugg...

Discovering and Visualizing Disease-Specific Electrocardiogram Features Using Deep Learning: Proof-of-Concept in Phospholamban Gene Mutation Carriers.

BACKGROUND: ECG interpretation requires expertise and is mostly based on physician recognition of sp...

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