Autonomic Nervous System Activity before Atrial Fibrillation Onset as Assessed by Heart Rate Variability.

Background: Neuromodulation has been shown to increase the efficacy of atrial fibrillation (AF) ablation procedures. However, despite its ability to influence the autonomic nervous system (ANS), the exact mechanism of action remains unclear. The activity of the ANS via the intracardiac nervous system (ICNS) can be inferred from heart rate variability (HRV).

Machine learning-based atrial fibrillation detection and onset prediction using QT-dynamicity.

. This study examines the value of ventricular repolarization using QT dynamicity for two different types of atrial fibrillation (AF) prediction..

IRIDIA-AF, a large paroxysmal atrial fibrillation long-term electrocardiogram monitoring database.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia in adults. Holter monitoring, a long-term 2-lead electrocardiogram (ECG), is a key tool available to cardiologists for AF diagnosis. Machine learning (ML) and deep learning (DL) models have shown great capacity to automatically detect AF in ECG and their use as medical decision support tool is growing.

Autonomic nervous system assessment using heart rate variability.

The role of the autonomic nervous system in the onset of supraventricular and ventricular arrhythmias is well established. It can be analysed by the spontaneous behaviour of the heart rate with ambulatory ECG recordings, through heart rate variability measurements. Input of heart rate variability parameters into artificial intelligence models to make predictions regarding the detection or forecast of rhythm disorders is becoming routine and neuromodulation techniques are now increasingly used for their treatment.

Role of the autonomic nervous system and premature atrial contractions in short-term paroxysmal atrial fibrillation forecasting: Insights from machine learning models.

Background: Machine learning and deep learning techniques are now used extensively for atrial fibrillation (AF) screening, but their use for AF crisis forecasting has yet to be assessed in a clinical context.

Aims: To assess the value of two machine learning algorithms for the short-term prediction of paroxysmal AF episodes.

Methods: We conducted a retrospective study from an outpatient clinic.