Cardiovascular

Myocardial Infarction

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

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Showing 1450-1470 of 7,963 articles
Interpretable AI for Time-Series: Multi-Model Heatmap Fusion with Global Attention and NLP-Generated Explanations

In this paper, we present a novel framework for enhancing model interpretability by integrating he...

Improving Myocardial Infarction Detection via Synthetic ECG Pretraining

Myocardial infarction is a major cause of death globally, and accurate early diagnosis from electr...

Masked Autoencoders that Feel the Heart: Unveiling Simplicity Bias for ECG Analyses

The diagnostic value of electrocardiogram (ECG) lies in its dynamic characteristics, ranging from ...

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...

Jun 2025 40417296
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...

Jun 2025 40391873
Federated Learning for MRI-based BrainAGE: a multicenter study on post-stroke functional outcome prediction

$\textbf{Objective:}$ Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflec...

Diffusion-Based Electrocardiography Noise Quantification via Anomaly Detection

Electrocardiography (ECG) signals are often degraded by noise, which complicates diagnosis in clin...

Deep Learning-Based Digitization of Overlapping ECG Images with Open-Source Python Code

This paper addresses the persistent challenge of accurately digitizing paper-based electrocardiogr...

Heartcare Suite: Multi-dimensional Understanding of ECG with Raw Multi-lead Signal Modeling

We present Heartcare Suite, a multimodal comprehensive framework for finegrained electrocardiogram...

PulseRide: A Robotic Wheelchair for Personalized Exertion Control with Human-in-the-Loop Reinforcement Learning

Maintaining an active lifestyle is vital for quality of life, yet challenging for wheelchair users...

Heart Rate Classification in ECG Signals Using Machine Learning and Deep Learning

This study addresses the classification of heartbeats from ECG signals through two distinct approa...

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...

Jun 2025 40157386
Uncertainty-Aware Multi-view Arrhythmia Classification from ECG

We propose a deep neural architecture that performs uncertainty-aware multi-view classification of...

anyECG-chat: A Generalist ECG-MLLM for Flexible ECG Input and Multi-Task Understanding

The advent of multimodal large language models (MLLMs) has sparked interest in their application t...

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....

Jun 2025 40524840
Cardiac Phase Estimation Using Deep Learning Analysis of Pulsed-Mode Projections: Toward Autonomous Cardiac CT Imaging.

Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its...

Jun 2025 40031322
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...

Jun 2025 40286395
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...

Jun 2025 40286493
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...

Jun 2025 40378565
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