Cardiovascular

Myocardial Infarction

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

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Unlocking the diagnostic potential of electrocardiograms through information transfer from cardiac magnetic resonance imaging.

Cardiovascular diseases (CVD) can be diagnosed using various diagnostic modalities. The electrocardi...

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

BACKGROUND: Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death i...

The Role of Machine Learning in the Detection of Cardiac Fibrosis in Electrocardiograms: Scoping Review.

BACKGROUND: Cardiovascular disease remains the leading cause of mortality worldwide. Cardiac fibrosi...

MrSeNet: Electrocardiogram signal denoising based on multi-resolution residual attention network.

Electrocardiography (ECG) is a widely used, non-invasive, and cost-effective diagnostic method that ...

Identifying the presence of atrial fibrillation during sinus rhythm using a dual-input mixed neural network with ECG coloring technology.

BACKGROUND: Undetected atrial fibrillation (AF) poses a significant risk of stroke and cardiovascula...

Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks.

Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for...

Arrhythmia Detection by Data Fusion of ECG Scalograms and Phasograms.

The automatic detection of arrhythmia is of primary importance due to the huge number of victims cau...

Advances in deep learning for personalized ECG diagnostics: A systematic review addressing inter-patient variability and generalization constraints.

The Electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretatio...

ECG-based machine learning model for AF identification in patients with first ischemic stroke.

BACKGROUND: The recurrence rate of strokes associated with atrial fibrillation (AF) can be substanti...

A novel ECG-based approach for classifying psychiatric disorders: Leveraging wavelet scattering networks.

Individuals with neuropsychiatric disorders experience both physical and mental difficulties, hinder...

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.

BACKGROUND: Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficienc...

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification....

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological diso...

Design and validation of Withings ECG Software 2, a tiny neural network based algorithm for detection of atrial fibrillation.

BACKGROUND: Atrial Fibrillation (AF) is the most common form of arrhythmia in the world with a preva...

Time-frequency transformation integrated with a lightweight convolutional neural network for detection of myocardial infarction.

Myocardial infarction (MI) is a life-threatening medical condition that necessitates both timely and...

Universal representations in cardiovascular ECG assessment: A self-supervised learning approach.

BACKGROUND: The 12-lead electrocardiogram (ECG) is an established modality for cardiovascular assess...

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