Myocardial Infarction

ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals.

. Sleep apnea is a common sleep disorder associated with severe health risks, necessitating accurate...

Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.

This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Per...

Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.

Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drive...

Artificial intelligence-enhanced ECG detection of acute coronary occlusion in chest pain patients with ST-elevation in lead aVR - A direct comparison to conventional ECG criteria.

Efficient pretraining of ECG scalogram images using masked autoencoders for cardiovascular disease diagnosis.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, emphasizing the need fo...

Artificial intelligence in cardiac sarcoidosis: ECG, Echo, CPET and MRI.

PURPOSE OF REVIEW: Cardiac sarcoidosis is a form of inflammatory cardiomyopathy that varies in its c...

Enhancing automatic multilabel diagnosis of electrocardiogram signals: A masked transformer approach.

BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools in c...

Artificial Intelligence-Enabled ECGs for Atrial Fibrillation Identification and Enhanced Oral Anticoagulant Adoption: A Pragmatic Randomized Clinical Trial.

BACKGROUND: Atrial fibrillation (AF) is often underdiagnosed and undertreated by noncardiologists. T...

Efficient sleep apnea detection using single-lead ECG: A CNN-Transformer-LSTM approach.

BACKGROUND: Sleep apnea (SA), a prevalent sleep-related breathing disorder, disrupts normal respirat...

Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: a scoping review.

BACKGROUND: Perioperative electrocardiographic monitoring can offer immediate detection of myocardia...

Beyond Recanalization: Machine Learning-Based Insights into Post-Thrombectomy Vascular Morphology in Stroke Patients.

Many stroke patients have poor outcomes despite successful endovascular therapy (EVT). We hypothesiz...

Semantic ECG hash similarity graph.

Graph-based methods have made significant progress in addressing the dependent correlations among EC...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Occult Atrial Fibrillation in Patients With Stroke Who Undergo Prolonged Cardiac Monitoring.

OBJECTIVE: To evaluate the performance of an artificial intelligence (AI)-enhanced electrocardiograp...

A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX).

Generative Counterfactual Explainable Artificial Intelligence (XAI) offers a novel approach to under...

Enhanced machine learning models for predicting three-year mortality in Non-STEMI patients aged 75 and above.

BACKGROUND: Non-ST segment elevation myocardial infarction (Non-STEMI) is a severe cardiovascular co...

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.

Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely in...

Metabolomic biomarkers could be molecular clocks in timing stroke onset.

The preferred treatment for acute ischaemic stroke (AIS) is intravenous thrombolysis (IVT) administe...

Machine learning evaluation model of pilot workload in a low-visibility environment.

To analyze the variation trend of pilots' workload in a low-visibility flight environment and then p...

In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction.

OBJECTIVE: The integration of predictive models into live clinical care requires scientific testing ...

Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.

While static risk models may identify key driving risk factors, the dynamic nature of risk requires ...

A Machine Learning Analysis of Physiological Monitoring Signals to Detect Small Airway Narrowing Due to Cold Air Exposure in Asthma.

Asthma is a chronic inflammatory disease of the small airways, affecting over 200 million people glo...