AIMC Topic: Electrocardiography

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Explainable artificial intelligence identifies and localizes left ventricular scar in hypertrophic cardiomyopathy using 12-Lead electrocardiogram.

Scientific reports
Left ventricular (LV) scar is a major risk factor for sudden death and heart failure in hypertrophic cardiomyopathy (HCM). LV scar evolves over time and needs longitudinal assessment. Currently, LV scar detection relies on late gadolinium enhancement...

Electrocardiogram heart rate variability for machine learning diagnosis of obstructive sleep Apnoea: A bayesian meta-analysis.

Sleep & breathing = Schlaf & Atmung
PURPOSE: Obstructive sleep apnoea syndrome (OSA) is a common yet underdiagnosed condition associated with significant health risks. Although polysomnography is the diagnostic gold standard, it is resource-intensive and unsuitable for widespread scree...

Deep learning model for diagnosing lupus erythematosus in cardiac patients using ECG and audio spectrograms.

Scientific reports
Individuals with both Lupus Erythematosus and pre-existing heart conditions are more likely to develop severe symptoms, emphasizing the complex and not fully understood interaction between the disease and cardiovascular health. A universal diagnostic...

Machine learning-based CAD detection using integrated ECG and PCG parameter features.

Biomedical physics & engineering express
The combined analysis of electrocardiogram (ECG) and phonocardiogram signals(PCG) has demonstrated significant potential in the non-invasive detection of coronary artery disease (CAD). The efficacy of combining cardiac pathological parameters such as...

Improved state refinement for LSTM determined 3D CAISR-LSTM model for automatic myocardial infarction detection.

Physiological measurement
Electrocardiograms (ECGs) contain valuable information in the clinical diagnosis of myocardial infarction (MI). However, its interpretation process is dependent on cardiologists with extensive clinical experience and expertise. The issue not only cau...

An intelligent diagnosis method for cardiovascular diseases based on the CNN-CBAM-GRU model.

PloS one
Early diagnosis of cardiovascular diseases (CVDs) is essential for improving patient outcomes. As a primary diagnostic modality, electrocardiogram (ECG) signals pose challenges for automatic classification due to their complex temporal and morphologi...

Phenotypic Selectivity of Artificial Intelligence-Enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction.

Circulation
BACKGROUND: Artificial intelligence (AI)-enhanced ECG (AI-ECG) models are often designed to detect specific anatomical and functional cardiac abnormalities. Understanding the selectivity of their phenotypic associations is essential to inform their c...

Integrating physiological signals for enhanced sleep apnea diagnosis with SleepNet.

Scientific reports
Sleep apnea, a prevalent respiratory disorder, poses significant health risks, including cardiovascular complications and behavioral issues, if left untreated. Traditional diagnostic methods like polysomnography, although effective, are often expensi...

Deep Unfolded Variable Projection Networks.

International journal of neural systems
In this paper, we present a hybrid learning framework that integrates two model-driven AI paradigms: Deep unfolding and Variable Projections (VPs). The core idea is to unfold the iterations of VP solvers for separable nonlinear least squares (SNLLS) ...

Principal component conditional generative adversarial networks for imbalanced ECG classification enhancement.

PloS one
With over a century of development, electrocardiogram (ECG) diagnostics has become the preferred tool for healthcare professionals in cardiovascular disease diagnosis and monitoring. As wearable devices and mobile monitoring technologies become wides...