Myocardial Infarction

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...

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...

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...

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...

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...

Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.

BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method i...

Pediatric Electrocardiogram-Based Deep Learning to Predict Secundum Atrial Septal Defects.

Secundum atrial septal defect (ASD2) detection is often delayed, with the potential for late diagnos...

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...

Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification

The automatic classification of medical time series signals, such as electroencephalogram (EEG) an...

GenECG: a synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.

OBJECTIVES: An image-based ECG dataset incorporating visual imperfections common to paper-based ECGs...

A Novel Coronary Artery Registration Method Based on Super-pixel Particle Swarm Optimization

Percutaneous Coronary Intervention (PCI) is a minimally invasive procedure that improves coronary ...

Beyond 1D: Vision Transformers and Multichannel Signal Images for PPG-to-ECG Reconstruction

Reconstructing ECG from PPG is a promising yet challenging task. While recent advancements in gene...

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular e...

An Open-Source Python Framework and Synthetic ECG Image Datasets for Digitization, Lead and Lead Name Detection, and Overlapping Signal Segmentation

We introduce an open-source Python framework for generating synthetic ECG image datasets to advanc...

Enhancing Contrastive Learning-based Electrocardiogram Pretrained Model with Patient Memory Queue

In the field of automatic Electrocardiogram (ECG) diagnosis, due to the relatively limited amount ...

CardioPatternFormer: Pattern-Guided Attention for Interpretable ECG Classification with Transformer Architecture

Accurate ECG interpretation is vital, yet complex cardiac data and "black-box" AI models limit cli...

Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework

Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) ...

[Artificial intelligence-based automated assessment of coronary flow reserve from angiography and the impact of different vasodilators].

To explore the feasibility of a coronary angiography-based method developed with artificial intelli...

Sensitivity of ECG QRS Complexes to His-Purkinje Structure in Computational Heart Models

Cardiac digital twins (CDT) are emerging as a potentially transformative tool in cardiology. A cri...

Acute myocardial infarction diagnosis: impact of artificial intelligence-powered ECG interpretation.

Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.

BACKGROUND AND AIMS: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (...