AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Electrocardiography

Showing 141 to 150 of 1241 articles

Clear Filters

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. H...

Bootstrap each lead's latent: A novel method for self-supervised learning of multilead electrocardiograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Electrocardiogram (ECG) is one of the most important diagnostic tools for cardiovascular diseases (CVDs). Recent studies show that deep learning models can be trained using labeled ECGs to achieve automatic detection of CVDs...

Serum Potassium Monitoring UsingĀ AI-Enabled Smartwatch Electrocardiograms.

JACC. Clinical electrophysiology
BACKGROUND: Hyperkalemia, characterized by elevated serum potassium levels, heightens the risk of sudden cardiac death, particularly increasing risk for individuals with chronic kidney disease and end-stage renal disease (ESRD). Traditional laborator...

Revolutionizing LVH detection using artificial intelligence: the AI heartbeat project.

Journal of hypertension
Many studies have shown the utility and promise of artificial intelligence (AI), for the diagnosis of left ventricular hypertrophy (LVH). The aim of the present study was to conduct a meta-analysis to compare the accuracy of AI tools to electrocardio...

ECG classification based on guided attention mechanism.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Integrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specif...

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Circulation journal : official journal of the Japanese Circulation Society
BACKGROUND: Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however...

Visual interpretation of deep learning model in ECG classification: A comprehensive evaluation of feature attribution methods.

Computers in biology and medicine
Feature attribution methods can visually highlight specific input regions containing influential aspects affecting a deep learning model's prediction. Recently, the use of feature attribution methods in electrocardiogram (ECG) classification has been...

Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling.

Journal of the American Heart Association
BACKGROUND: We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs immediately before AF occurrence. Our aim i...

An Arrhythmia Classification Model Based on a CNN-LSTM-SE Algorithm.

Sensors (Basel, Switzerland)
Arrhythmia is the main cause of sudden cardiac death, and ECG signal analysis is a common method for the noninvasive diagnosis of arrhythmia. In this paper, we propose an arrhythmia classification model based on the combination of a channel attention...

Deep Learning-based 12-Lead Electrocardiogram for Low Left Ventricular Ejection Fraction Detection in Patients.

The Canadian journal of cardiology
BACKGROUND: Reduced left ventricular ejection fraction (LVEF) initiates heart failure, and promptly identifying low ejection fraction is crucial for managing progression and averting mortality. In this study we developed an artificial intelligence-en...