AIMC Topic: Electrocardiography
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Research on noninvasive electrophysiologic imaging based on cardiac electrophysiology simulation and deep learning methods for the inverse problem.
BMC cardiovascular disorders
Apr 28, 2025
BACKGROUND: The risk stratification and prognosis of cardiac arrhythmia depend on the individual condition of patients, while invasive diagnostic methods may be risky to patient health, and current non-invasive diagnostic methods are applicable to fe...
Heart failure monitoring with a single‑lead electrocardiogram at home.
International journal of cardiology
Apr 24, 2025
BACKGROUND: Repeated hospitalization due to heart failure (HF) is a significant predictor of mortality. However, there are limited early detection systems for HF progression that can be utilized by patients at home without a cardiac implantable elect...
Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.
BMC pediatrics
Apr 24, 2025
BACKGROUND: Congenital heart disease (CHD) affects approximately 1% of newborns and is a leading cause of mortality in early childhood. Despite the importance of early detection, current screening methods, such as pulse oximetry and auscultation, hav...
FADE: Forecasting for anomaly detection on ECG.
Computer methods and programs in biomedicine
Apr 22, 2025
BACKGROUND AND OBJECTIVE: Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances in machine learning and deep learning, multip...
Machine learning to risk stratify chest pain patients with non-diagnostic electrocardiogram in an Asian emergency department.
Annals of the Academy of Medicine, Singapore
Apr 22, 2025
INTRODUCTION: Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers a...
Development of an Artificial Intelligence-Enabled Electrocardiography to Detect 23 Cardiac Arrhythmias and Predict Cardiovascular Outcomes.
Journal of medical systems
Apr 22, 2025
Arrhythmias are common and can affect individuals with or without structural heart disease. Deep learning models (DLMs) have shown the ability to recognize arrhythmias using 12-lead electrocardiograms (ECGs). However, the limited types of arrhythmias...
Machine-learning guided differentiation between photoplethysmography waveforms of supraventricular and ventricular origin.
Computer methods and programs in biomedicine
Apr 20, 2025
BACKGROUND: It is unclear, whether photoplethysmography (PPG) waveforms from wearable devices can differentiate between supraventricular and ventricular arrhythmias. We assessed, whether a neural network-based classifier can distinguish the origin of...
AI analysis for ejection fraction estimation from 12-lead ECG.
Scientific reports
Apr 18, 2025
Heart failure (HF) remains a leading global cause of cardiovascular deaths, with its prevalence expected to rise in the upcoming decade. Measuring the heart ejection fraction (EF) is crucial for diagnosing and monitoring HF. Although echocardiography...
A hybrid machine learning approach using particle swarm optimization for cardiac arrhythmia classification.
International journal of cardiology
Apr 11, 2025
BACKGROUND: Precise and rapid identification of cardiac arrhythmias is paramount for delivering optimal patient care. Machine learning (ML) techniques hold significant promise for classifying arrhythmias, yet achieving peak performance often necessit...
Clinical-level screening of sleep apnea syndrome with single-lead ECG alone is achievable using machine learning with appropriate time windows.
Sleep & breathing = Schlaf & Atmung
Apr 11, 2025
PURPOSE: To establish a simple and noninvasive screening test for sleep apnea (SA) that imposes less burden on potential patients. The specific objective of this study was to verify the effectiveness of past and future single-lead electrocardiogram (...
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