Myocardial Infarction

A Computational Framework for Predicting Novel Drug Indications Using Graph Convolutional Network With Contrastive Learning.

Inferring potential drug indications plays a vital role in the drug discovery process. It can be time-consuming and costly to discover novel drug indications through biological experiments. Recently, graph learning-based methods have gained popularity for this task. These methods typically treat the prediction task as a binary classification problem, focusing on modeling associations between drugs...

MPCNN: A Novel Matrix Profile Approach for CNN-based Single Lead Sleep Apnea in Classification Problem.

Sleep apnea (SA) is a significant respiratory condition that poses a major global health challenge. Deep Learning (DL) has emerged as an efficient tool for the classification problem in electrocardiogram (ECG)-based SA diagnoses. Despite these advancements, most common conventional feature extractions derived from ECG signals in DL, such as R-peaks and RR intervals, may fail to capture crucial inf...

Rationale and design of the artificial intelligence scalable solution for acute myocardial infarction (ASSIST) study.

BACKGROUND: Acute coronary syndrome (ACS), specifically ST-segment elevation myocardial infarction is a major cause of morbidity and mortality through...

Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improv...

Development of Clinically Validated Artificial Intelligence Model for Detecting ST-segment Elevation Myocardial Infarction.

STUDY OBJECTIVE: Although the importance of primary percutaneous coronary intervention has been emphasized for ST-segment elevation myocardial infarct...

Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.

Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/P...

Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.

BACKGROUND: Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the e...

Artificial Intelligence-Enabled Electrocardiography Predicts Future Pacemaker Implantation and Adverse Cardiovascular Events.

Medical advances prolonging life have led to more permanent pacemaker implants. When pacemaker implantation (PMI) is commonly caused by sick sinus syn...

A machine-learning approach for stress detection using wearable sensors in free-living environments.

Stress is a psychological condition resulting from the body's response to challenging situations, which can negatively impact physical and mental heal...

Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning.

. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization str...

Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.

Heart failure (HF) is defined as the inability of the heart to meet body oxygen demand requiring an elevation in left ventricular filling pressures (L...

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG...

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients ...

Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections.

The aim of this study is to address the challenge of 12-lead ECG delineation by different encoder-decoder architectures of deep neural networks (DNNs)...

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN.

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electro...

Enhancing ECG Heartbeat classification with feature fusion neural networks and dynamic minority-biased batch weighting loss function.

This study aims to address the challenges of imbalanced heartbeat classification using electrocardiogram (ECG). In this proposed novel deep-learning m...

Uncovering STEMI patient phenotypes using unsupervised machine learning.

Diagnostic and Prognostic Electrocardiogram-Based Models for Rapid Clinical Applications.

Leveraging artificial intelligence (AI) for the analysis of electrocardiograms (ECGs) has the potential to transform diagnosis and estimate the progno...

A novel way to prospectively evaluate of AI-enhanced ECG algorithms.

Significant strides will be made in the field of computerized electrocardiology through the development of artificial intelligence (AI)-enhanced ECG (...

A Novel Instruction Driven 1-D CNN Processor for ECG Classification.

Electrocardiography (ECG) has emerged as a ubiquitous diagnostic tool for the identification and characterization of diverse cardiovascular pathologie...