Cardiovascular

Myocardial Infarction

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

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Showing 1534-1554 of 7,976 articles
Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB

A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially pow...

Enhanced ECG Arrhythmia Detection Accuracy by Optimizing Divergence-Based Data Fusion

AI computation in healthcare faces significant challenges when clinical datasets are limited and h...

Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances ...

Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study.

BACKGROUND AND AIMS: Current heart failure (HF) risk stratification strategies require comprehensive...

Mar 2025 39804243
Neuro-Informed Adaptive Learning (NIAL) Algorithm: A Hybrid Deep Learning Approach for ECG Signal Classification

The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early ...

Towards Hardware Supported Domain Generalization in DNN-Based Edge Computing Devices for Health Monitoring

Deep neural network (DNN) models have shown remarkable success in many real-world scenarios, such ...

TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis

Electrocardiogram (ECG) signals are widely shared across multiple clinical applications for diagno...

Are ECGs enough? Deep learning classification of cardiac anomalies using only electrocardiograms

Electrocardiography (ECG) is an essential tool for diagnosing multiple cardiac anomalies: it provi...

A Systematic Review of ECG Arrhythmia Classification: Adherence to Standards, Fair Evaluation, and Embedded Feasibility

The classification of electrocardiogram (ECG) signals is crucial for early detection of arrhythmia...

GEM: Empowering MLLM for Grounded ECG Understanding with Time Series and Images

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, ...

Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMIC...

Conditional Electrocardiogram Generation Using Hierarchical Variational Autoencoders

Cardiovascular diseases (CVDs) are disorders impacting the heart and circulatory system. These dis...

ECG-EmotionNet: Nested Mixture of Expert (NMoE) Adaptation of ECG-Foundation Model for Driver Emotion Recognition

Driver emotion recognition plays a crucial role in driver monitoring systems, enhancing human-auto...

Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.

BACKGROUND AND AIMS: Robust and convenient risk stratification of patients with paediatric and adult...

Mar 2025 39387652
Artificial intelligence-derived electrocardiographic aging and risk of atrial fibrillation: a multi-national study.

BACKGROUND AND AIMS: Artificial intelligence (AI) algorithms in 12-lead electrocardiogram (ECG) prov...

Mar 2025 39626169
OpenECG: Benchmarking ECG Foundation Models with Public 1.2 Million Records

This study introduces OpenECG, a large-scale benchmark of 1.2 million 12-lead ECG recordings from ...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those wi...

Mar 2025 39813064
Parallel-Learning of Invariant and Tempo-variant Attributes of Single-Lead Cardiac Signals: PLITA

Wearable sensing devices, such as Holter monitors, will play a crucial role in the future of digit...

egoPPG: Heart Rate Estimation from Eye-Tracking Cameras in Egocentric Systems to Benefit Downstream Vision Tasks

Egocentric vision systems aim to understand the spatial surroundings and the wearer's behavior ins...

SuPreME: A Supervised Pre-training Framework for Multimodal ECG Representation Learning

Cardiovascular diseases are a leading cause of death and disability worldwide. Electrocardiogram (...

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