ArticlePloS one2022
Accurate detection of atrial fibrillation events with R-R intervals from ECG signals.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Autonomic Signature-Driven Anesthesia Depth Monitoring with Biomimetic Wearable ECG and Knowledge Graph-Augmented Deep Networks.Sensors (Basel, Switzerland) · 2026Article
- Clinical performance of an atrial fibrillation burden tracking algorithm: Evaluation against reference public and clinical textile electrocardiographic datasets.Heart rhythm O2 · 2026Article
- Oxidative Stress, Mitochondrial Homeostasis, and Sirtuins in Atrial Fibrillation.International journal of molecular sciences · 2025Review
- Paradoxical SERCA2a Dysregulation Contributes to Atrial Fibrillation in a Model of Diet-Induced Obesity.International journal of molecular sciences · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Detection of Atrial Fibrillation Episodes based on 3D Algebraic Relationships between Cardiac Intervals.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Atrial fibrillation (AF) is a typical category of arrhythmia. Clinical diagnosis of AF is based on the detection of abnormal R-R intervals (RRIs) with an electrocardiogram (ECG). Previous studies considered this detection problem as a classification problem and focused on extracting a number of features. In this study we demonstrate that instead of using any specific numerical characteristic as the input feature, the probability density of RRIs from ECG conserves comprehensive statistical information; hence, is a natural and efficient input feature for AF detection. Incorporated with a support vector machine as the classifier, results on the MIT-BIH database indicates that the proposed method is a simple and accurate approach for AF detection in terms of accuracy, sensitivity, and specificity.
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