ArticleSensors (Basel, Switzerland)2020
Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Detecting Atrial Fibrillation from ECGs: Systematic Review and Meta-Analysis.Reviews in cardiovascular medicine · 2024Pooled it
- Clinical performance of an atrial fibrillation burden tracking algorithm: Evaluation against reference public and clinical textile electrocardiographic datasets.Heart rhythm O2 · 2026Article
- Explainable Federated Learning for Multi-Class Heart Disease Diagnosis via ECG Fiducial Features.Diagnostics (Basel, Switzerland) · 2025Article
- Heart rate variability in patients with atrial fibrillation of sinus rhythm or atrial fibrillation: chaos or merit?Annals of medicine · 2025Review
- HSPD1 regulates angiotensin II‑induced atrial fibrillation via the PPAR signaling pathway.Molecular medicine reports · 2025Article
- AF-DETR: Transformer-Based Object Detection for Precise Atrial Fibrillation Beat Localization in ECG.Bioengineering (Basel, Switzerland) · 2025Article
- Robust screening of atrial fibrillation with distribution classification.Scientific reports · 2025Article
- Episode-level and clinical characterization of asymptomatic atrial fibrillation events.Journal of cardiovascular electrophysiology · 2024Article
- mRMEBP: a unified framework for online detection of atrial fibrillation utilizing deep learning.npj biomedical innovations · 2024Article
- Compressed Deep Learning Models for Wearable Atrial Fibrillation Detection through Attention.Sensors (Basel, Switzerland) · 2024Article
- Article
- Knowledge, attitude, and practice of atrial fibrillation in high altitude areas.Frontiers in public health · 2024Article
- A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG.Diagnostics (Basel, Switzerland) · 2023Article
- Different Ventricular Fibrillation Types in Low-Dimensional Latent Spaces.Sensors (Basel, Switzerland) · 2023Article
- Wearable Devices Combined with Artificial Intelligence-A Future Technology for Atrial Fibrillation Detection?Sensors (Basel, Switzerland) · 2022Article
- Accurate detection of atrial fibrillation events with R-R intervals from ECG signals.PloS one · 2022Article
- A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia.Sensors (Basel, Switzerland) · 2021Article
- Learning Explainable Time-Morphology Patterns for Automatic Arrhythmia Classification from Short Single-Lead ECGs.Sensors (Basel, Switzerland) · 2021Article
- Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation.Frontiers in neuroscience · 2021Article
- Recent updates on novel therapeutic targets of cardiovascular diseases.Molecular and cellular biochemistry · 2021Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.