ArticleDiagnostics (Basel, Switzerland)2021
Automated Arrhythmia Detection Based on RR Intervals.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Clinically-aligned explainable AI for atrial fibrillation detection: A U-Net inspired multi-lead ECG analysis framework.Computer methods and programs in biomedicine · 2026Article
- Music-Inspired MK545Pat for AF/PAF/Non-AF Classification Using ECG Signals.Bioengineering (Basel, Switzerland) · 2026Article
- Machine learning-enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support.Scientific reports · 2026Article
- 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
- Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.Sensors (Basel, Switzerland) · 2024Article
- Performance Evaluation of Quantum-Based Machine Learning Algorithms for Cardiac Arrhythmia Classification.Diagnostics (Basel, Switzerland) · 2023Article
- ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Article
- Wearable Devices for Remote Monitoring of Heart Rate and Heart Rate Variability-What We Know and What Is Coming.Sensors (Basel, Switzerland) · 2022Review
- Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?Sensors (Basel, Switzerland) · 2022Article
- Detecting paroxysmal atrial fibrillation from normal sinus rhythm in equine athletes using Symmetric Projection Attractor Reconstruction and machine learning.Cardiovascular digital health journal · 2022Article
- A Novel Deep-Learning-Based Framework for the Classification of Cardiac Arrhythmia.Journal of imaging · 2022Article
- Hybrid Decision Support to Monitor Atrial Fibrillation for Stroke Prevention.International journal of environmental research and public health · 2021Article
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Authors and funding
5 authors.
Funding
Abstract
Abnormal heart rhythms, also known as arrhythmias, can be life-threatening. AFIB and AFL are examples of arrhythmia that affect a growing number of patients. This paper describes a method that can support clinicians during arrhythmia diagnosis. We propose a deep learning algorithm to discriminate AFIB, AFL, and NSR RR interval signals. The algorithm was designed with data from 4051 subjects. With 10-fold cross-validation, the algorithm achieved the following results: ACC = 99.98%, SEN = 100.00%, and SPE = 99.94%. These results are significant because they show that it is possible to automate arrhythmia detection in RR interval signals. Such a detection method makes economic sense because RR interval signals are cost-effective to measure, communicate, and process. Having such a cost-effective solution might lead to widespread long-term monitoring, which can help detecting arrhythmia earlier. Detection can lead to treatment, which improves outcomes for patients.
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Registered trials
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