Cardiovascular

Myocardial Infarction

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

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Showing 106-126 of 6,871 articles
FADE: Forecasting for anomaly detection on ECG.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-relate...

Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.

INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be p...

AI analysis for ejection fraction estimation from 12-lead ECG.

Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expe...

Improved security for IoT-based remote healthcare systems using deep learning with jellyfish search optimization algorithm.

With an increased chronic disease and an ageing population, remote health monitoring is a substantia...

Bi-variational physics-informed operator network for fractional flow reserve curve assessment from coronary angiography.

The coronary angiography-derived fractional flow reserve (FFR) curve, referred to as the Angio-FFR c...

Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.

PURPOSE: To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less...

Enhanced electrocardiogram classification using Gramian angular field transformation with multi-lead analysis and segmentation techniques.

Conventional manual or feature-based ECG analysis methods are limited by time inefficiencies and hum...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from t...

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an in...

ECG Sensor Design Assessment with Variational Autoencoder-Based Digital Watermarking.

Designing an ECG sensor circuit requires a comprehensive approach to detect, amplify, filter, and co...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...

An Energy-Efficient Configurable 1-D CNN-Based Multi-Lead ECG Classification Coprocessor for Wearable Cardiac Monitoring Devices.

Many electrocardiogram (ECG) processors have been widely used for cardiac monitoring. However, most ...

Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A Systematic Review.

BACKGROUND: Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) ...

Electrocardiographic Discrimination of Long QT Syndrome Genotypes: A Comparative Analysis and Machine Learning Approach.

Long QT syndrome (LQTS) presents a group of inheritable channelopathies with prolonged ventricular r...

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study.

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analys...

Integrating deep learning with ECG, heart rate variability and demographic data for improved detection of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a common but often undiagnosed condition, increasing the ris...

Pathophysiological mechanisms of exertional dyspnea in people with cardiopulmonary disease: Recent advances.

Physical activity is a leading trigger of dyspnea in chronic cardiopulmonary diseases. Recently, the...

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