Cardiovascular

Myocardial Infarction

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

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Showing 274-294 of 6,871 articles
Image-based ECG analyzing deep-learning algorithm to predict biological age and mortality risks: interethnic validation.

BACKGROUND: Cardiovascular risk assessment is a critical component of healthcare, guiding preventive...

The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.

BACKGROUND: Anomalous origin of coronary artery is a common coronary artery anatomy anomaly. The ano...

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection.

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and address...

Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism.

Deep learning has shown great promise in predicting Atrial Fibrillation using ECG signals and other ...

A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.

The multi-lead electrocardiogram (ECG) is widely utilized in clinical diagnosis and monitoring of ca...

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and ...

SeqAFNet: A Beat-Wise Sequential Neural Network for Atrial Fibrillation Classification in Adhesive Patch-Type Electrocardiographs.

Due to their convenience, adhesive patch-type electrocardiographs are commonly used for arrhythmia s...

RawECGNet: Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG.

INTRODUCTION: Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm i...

Conv-RGNN: An efficient Convolutional Residual Graph Neural Network for ECG classification.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) analysis is crucial in diagnosing cardiovascular d...

Detection and classification of electrocardiography using hybrid deep learning models.

OBJECTIVE: Electrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnos...

ECG classification via integration of adaptive beat segmentation and relative heart rate with deep learning networks.

We propose a state-of-the-art deep learning approach for accurate electrocardiogram (ECG) signal ana...

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke.

BACKGROUND: The objective of this study was to establish a predictive model utilizing machine learni...

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. Thi...

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones.

BACKGROUND: Researchers have developed machine learning-based ECG diagnostic algorithms that match o...

Applying masked autoencoder-based self-supervised learning for high-capability vision transformers of electrocardiographies.

The generalization of deep neural network algorithms to a broader population is an important challen...

A Deep-Learning-Enabled Electrocardiogram and Chest X-Ray for Detecting Pulmonary Arterial Hypertension.

The diagnosis and treatment of pulmonary hypertension have changed dramatically through the re-defin...

Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.

Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its ...

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