AIMC Topic: Electrocardiography, Ambulatory
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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...
Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.
Scientific reports
Apr 7, 2025
Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from the traditional 24 h to 7-14 days, significantly enriching ECG data. However, this poses unprecedented challenges for physicians in analyzing these ext...
Circadian rhythm modulation in heart rate variability as potential biomarkers for major depressive disorder: A machine learning approach.
Journal of psychiatric research
Mar 4, 2025
Major depressive disorder (MDD) is associated with reduced heart rate variability (HRV), but its link to circadian rhythm modulation (CRM) of HRV is unclear. Given that depression disrupts circadian rhythms, assessing HRV fluctuations may better capt...
Artificial intelligence for direct-to-physician reporting of ambulatory electrocardiography.
Nature medicine
Feb 10, 2025
Developments in ambulatory electrocardiogram (ECG) technology have led to vast amounts of ECG data that currently need to be interpreted by human technicians. Here we tested an artificial intelligence (AI) algorithm for direct-to-physician reporting ...
Residual-attention deep learning model for atrial fibrillation detection from Holter recordings.
Journal of electrocardiology
Jan 10, 2025
BACKGROUND: Detecting subtle patterns of atrial fibrillation (AF) and irregularities in Holter recordings is intricate and unscalable if done manually. Artificial intelligence-based techniques can be beneficial. In fact, with the rapid advancement of...
Using Atrial Fibrillation Burden Trends and Machine Learning to Predict Near-Term Risk of Cardiovascular Hospitalization.
Circulation. Arrhythmia and electrophysiology
Oct 24, 2024
BACKGROUND: Atrial fibrillation is associated with an increased risk of cardiovascular hospitalization (CVH), which may be triggered by changes in daily burden. Machine learning of dynamic trends in atrial fibrillation burden, as measured by insertab...
Automatic noise detection for ambulatory electrocardiogram in presence of ventricular arrhythmias through a machine learning approach.
Computers in biology and medicine
Oct 23, 2024
Noise detection in ambulatory electrocardiography is investigated as a machine learning binary classification problem on a set of twelve noise indices. Ten of these noise indices are replicated from relevant scientific literature. Two novel noise ind...
Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care.
BMC primary care
Jul 20, 2024
BACKGROUND: Atrial fibrillation (AF) is highly correlated with heart failure, stroke and death. Screening increases AF detection and facilitates the early adoption of comprehensive intervention. Long-term wearable devices have become increasingly pop...
Sleep-phasic heart rate variability predicts stress severity: Building a machine learning-based stress prediction model.
Stress and health : journal of the International Society for the Investigation of Stress
Feb 27, 2024
We propose a novel approach for predicting stress severity by measuring sleep phasic heart rate variability (HRV) using a smart device. This device can potentially be applied for stress self-screening in large populations. Using a Holter electrocardi...
Monitoring of Remotely Reprogrammable Implantable Loop Recorders With Algorithms to Reduce False-Positive Alerts.
Journal of the American Heart Association
Feb 23, 2024
BACKGROUND: Implantable loop recorders (ILRs) are increasingly placed for arrhythmia detection. However, historically, ≈75% of ILR alerts are false positives, requiring significant time and effort for adjudication. The LINQII and LUX-Dx are remotely ...
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