Myocardial Infarction

Age prediction from 12-lead electrocardiograms using deep learning: a comparison of four models on a contemporary, freely available dataset.

The 12-lead electrocardiogram (ECG) is routine in clinical use and deep learning approaches have bee...

Deep learning-based multimodal fusion of the surface ECG and clinical features in prediction of atrial fibrillation recurrence following catheter ablation.

BACKGROUND: Despite improvement in treatment strategies for atrial fibrillation (AF), a significant ...

A Novel Real-Time Detection and Classification Method for ECG Signal Images Based on Deep Learning.

In this paper, a novel deep learning method Mamba-RAYOLO is presented, which can improve detection a...

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. ...

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 tim...

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 i...

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 sen...

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 emph...

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) ...

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. Scre...

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 impla...

Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

BACKGROUND: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive c...

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, wh...

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

. Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a signifi...

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 ...

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 aut...

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-de...

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...

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 compli...

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 electrocardio...

Uncovering STEMI patient phenotypes using unsupervised machine learning.