AIMC Topic: Atrial Fibrillation
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SEResUTer: a deep learning approach for accurate ECG signal delineation and atrial fibrillation detection.
Physiological measurement
Dec 15, 2023
Accurate detection of electrocardiogram (ECG) waveforms is crucial for computer-aided diagnosis of cardiac abnormalities. This study introduces SEResUTer, an enhanced deep learning model designed for ECG delineation and atrial fibrillation (AF) detec...
Validation of a Deep Learning Algorithm for Continuous, Real-Time Detection of Atrial Fibrillation Using a Wrist-Worn Device in an Ambulatory Environment.
Journal of the American Heart Association
Sep 26, 2023
BACKGROUND: Wearable devices may be useful for identification, quantification and characterization, and management of atrial fibrillation (AF). To date, consumer wrist-worn devices for AF detection using photoplethysmography-based algorithms perform ...
Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.
Hereditas
Aug 24, 2023
BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in ...
Machine Learning Predicting Atrial Fibrillation as an Adverse Event in the Warfarin and Aspirin in Reduced Cardiac Ejection Fraction (WARCEF) Trial.
The American journal of medicine
Aug 21, 2023
BACKGROUND: Atrial fibrillation and heart failure commonly coexist due to shared pathophysiological mechanisms. Prompt identification of patients with heart failure at risk of developing atrial fibrillation would allow clinicians the opportunity to i...
Classification of electrocardiogram signals using deep learning based on genetic algorithm feature extraction.
Biomedical physics & engineering express
Jul 31, 2023
Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive ...
On Merging Feature Engineering and Deep Learning for Diagnosis, Risk Prediction and Age Estimation Based on the 12-Lead ECG.
IEEE transactions on bio-medical engineering
Jun 19, 2023
OBJECTIVE: Over the past few years, deep learning (DL) has been used extensively in research for 12-lead electrocardiogram (ECG) analysis. However, it is unclear whether the explicit or implicit claims made on DL superiority to the more classical fea...
Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms.
Circulation. Genomic and precision medicine
Jun 6, 2023
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually not well u...
Erroneous electrocardiographic interpretations and its clinical implications.
Journal of cardiovascular electrophysiology
Jun 5, 2023
INTRODUCTION: The advancement of artificial intelligence (AI) has aided clinicians in the interpretation of electrocardiograms (ECGs) serving as an essential tool to provide rapid triage and care. However, in some cases, AI can misinterpret an ECG an...
Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age.
Mayo Clinic proceedings
Feb 11, 2023
OBJECTIVE: To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.
Deep-Learning for Epicardial Adipose Tissue Assessment With Computed Tomography: Implications for Cardiovascular Risk Prediction.
JACC. Cardiovascular imaging
Feb 8, 2023
BACKGROUND: Epicardial adipose tissue (EAT) volume is a marker of visceral obesity that can be measured in coronary computed tomography angiograms (CCTA). The clinical value of integrating this measurement in routine CCTA interpretation has not been ...
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