Myocardial Infarction

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding.

This study aims to introduce a novel, computationally lightweight feature extraction technique calle...

Predicting prolonged length of in-hospital stay in patients with non-ST elevation myocardial infarction (NSTEMI) using artificial intelligence.

BACKGROUND: Patients presenting with non-ST elevation myocardial infarction (NSTEMI) are typically e...

State-of-the-art analysis of electrocardiogram findings in sudden cardiac death.

Sudden cardiac death (SCD) is a significant public health issue, and efforts to prevent it have invo...

Detection of large areas of thin-cap fibroatheroma in a recurrent STEMI patient using a novel artificial intelligence algorithm: moving from 2D to 3D.

Predictive Modeling of Heart Failure Outcomes Using ECG Monitoring Indicators and Machine Learning.

BACKGROUND: Heart failure (HF) is a major driver of global morbidity and mortality. Early identifica...

Myocardial Infarction Detection using Variational Mode Decomposition with Fuzzy Weight Particle Swarm Optimization and Depthwise Separable Convolutional Network.

The challenge of precisely recognizing myocardial infarction (MI) from electrocardiographic (ECG) re...

Error correcting 2D-3D cascaded network for myocardial infarct scar segmentation on late gadolinium enhancement cardiac magnetic resonance images.

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is considered the in vivo...

ASSOCIATIONS BETWEEN HEART RATE VARIABILITY AND NEED FOR LIFESAVING INTERVENTION IN A LARGE HELICOPTER EMS SERVICE.

Background : Heart rate variability (HRV) measures give insight into the autonomic regulation of car...

Re-engineering the clinical approach to suspected cardiac chest pain assessment in the emergency department by expediting research evidence to practice using artificial intelligence. (RAPIDx AI)-a cluster randomized study design.

BACKGROUND: Clinical work-up for suspected cardiac chest pain is resource intensive. Despite expecta...

MACHINE LEARNING AND SHOCK INDICES-DERIVED SCORE FOR PREDICTING CONTRAST-INDUCED NEPHROPATHY IN ACUTE CORONARY SYNDROME PATIENTS.

Background: Contrast-induced nephropathy (CIN) is a serious complication following acute coronary sy...

Hybrid strategy of coronary atherosclerosis characterization with T1-weighted MRI and CT angiography to non-invasively predict periprocedural myocardial injury.

AIMS: Coronary computed tomography angiography (CCTA) and magnetic resonance imaging (MRI) can predi...

Accurate and efficient machine learning interatomic potentials for finite temperature modelling of molecular crystals.

As with many parts of the natural sciences, machine learning interatomic potentials (MLIPs) are revo...

Preparation of Active On-Demand Antibacterial Hydrogel Epidermis Electrodes Based on Flora Balance Strategy for Intelligent Prostheses.

Hydrogel epidermis electrodes have demonstrated remarkable potential for stable electrophysiological...

Near-term prediction of sustained ventricular arrhythmias applying artificial intelligence to single-lead ambulatory electrocardiogram.

BACKGROUND AND AIMS: Accurate near-term prediction of life-threatening ventricular arrhythmias would...

Large Language Model-informed ECG Dual Attention Network for Heart Failure Risk Prediction.

Heart failure (HF) poses a significant public health challenge, with a rising global mortality rate....

QRS-centric beat-wise atrial fibrillation detection in ECG signals using deep neural networks.

We propose a deep learning approach for beat-wise atrial fibrillation (AF) detection in electrocardi...

Diagnostic accuracy of machine learning algorithms in electrocardiogram-based sleep apnea detection: A systematic review and meta-analysis.

Sleep apnea is a prevalent disorder affecting 10 % of middle-aged individuals, yet it remains underd...

Faster R-CNN approach for estimating global QRS duration in electrocardiograms with a limited quantity of annotated data.

In electrocardiography (ECG), measurement of QRS duration (QRSd) is crucial for diagnosing condition...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming...

Portable ECG and PCG wireless acquisition system and multiscale CNN feature fusion Bi-LSTM network for coronary artery disease diagnosis.

Coronary artery disease (CAD) is a major cause of mortality, especially among aging populations, mak...

A multi-scale convolutional LSTM-dense network for robust cardiac arrhythmia classification from ECG signals.

Cardiac arrhythmias are irregular heart rhythms that, if undetected, can lead to severe cardiovascul...