Cardiovascular

Myocardial Infarction

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

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Explainable Multimodal Deep Learning for Heart Sounds and Electrocardiogram Classification.

We introduce a Gradient-weighted Class Activation Mapping (Grad-CAM) methodology to assess the perfo...

Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness.

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare a...

ECG-based Daily Activity Recognition Using 1D Convolutional Neural Networks.

This study presents an approach to human activity recognition (HAR) using electrocardiogram (ECG) si...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancem...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditio...

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is...

Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.

AIMS: Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic a...

Clinical named entity recognition for percutaneous coronary intervention surgical information with hybrid neural network.

Percutaneous coronary intervention (PCI) has become a vital treatment approach for coronary artery d...

AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sud...

Biometric contrastive learning for data-efficient deep learning from electrocardiographic images.

OBJECTIVE: Artificial intelligence (AI) detects heart disease from images of electrocardiograms (ECG...

Deep Learning-Augmented ECG Analysis for Screening and Genotype Prediction of Congenital Long QT Syndrome.

IMPORTANCE: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, ...

Arrhythmia classification based on multi-feature multi-path parallel deep convolutional neural networks and improved focal loss.

Early diagnosis of abnormal electrocardiogram (ECG) signals can provide useful information for the p...

Convolutional transformer-driven robust electrocardiogram signal denoising framework with adaptive parametric ReLU.

The electrocardiogram (ECG) is a widely used diagnostic tool for cardiovascular diseases. However, E...

Heart Rate and its Variability From Short-Term ECG Recordings as Potential Biomarkers for Detecting Mild Cognitive Impairment.

Alterations in Heart Rate (HR) and Heart Rate Variability (HRV) reflect autonomic dysfunction assoc...

An automated ECG-based deep learning for the early-stage identification and classification of cardiovascular disease.

BACKGROUND: Heart disease represents the leading cause of death globally. Timely diagnosis and treat...

Mental fatigue recognition study based on 1D convolutional neural network and short-term ECG signals.

BACKGROUND: Mental fatigue has become a non-negligible health problem in modern life, as well as one...

Multimodality Risk Assessment of Patients with Ischemic Heart Disease Using Deep Learning Models Applied to Electrocardiograms and Chest X-rays.

Comprehensive management approaches for patients with ischemic heart disease (IHD) are important aid...

Improved diagnostic performance of insertable cardiac monitors by an artificial intelligence-based algorithm.

AIMS: The increasing use of insertable cardiac monitors (ICM) produces a high rate of false positive...

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