Key result
The proposed Support Vector Machine with Radial Basis Function using multi-segment RR intervals achieved an accuracy of 99.17% for detecting paroxysmal atrial fibrillation.
Why the study?
Early detection of paroxysmal atrial fibrillation episodes is important to provide appropriate treatment and reduce the associated risks of stroke, mortality, and complications.
Does a Support Vector Machine with Radial Basis Function using multi-segment RR interval statistics improve the recognition of paroxysmal atrial fibrillation?
Does a Support Vector Machine with Radial Basis Function using multi-segment RR interval statistics improve the recognition of paroxysmal atrial fibrillation?
A Support Vector Machine model utilizing statistical features of multi-segment RR intervals can detect paroxysmal atrial fibrillation with over 99% accuracy.
Answers are grounded in this paper and cite their sources.
Promising accuracy supports further SVM-RBF development for paroxysmal AF detection; leaves open prospective clinical validation.
Paroxysmal Atrial fibrillation (PAF) is a heart problem relating to irregular and rapid beating of the heart atria. It has risk of stroke and is independently associated with risk of mortality. Early information of PAF episode is important for a patient to have appropriate treatment to reduce atrial fibrillation complications. This article presents a new strategy to detect PAF with base of statistical electrocardiographic features and a support vector machine (SVM). R-peak series of electrocardiogram were segmented and were extracted to find the statistics of RR intervals. Different approaches in relation with the segmentation were investigated. Two-class SVM with radial basis function (RBF) and the statistics of RR intervals were examined for PAF detection. Using clinical data of patients with PAF, the proposed strategy showed excellent performance of 99.17% in terms of accuracy. The experimental result indicated that the appropriate statistics of RR intervals and SVM-RBF with its suitable parameters can perform well for PAF detection.
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Nuryani Nuryani (2020) studied Paroxysmal Atrial Fibrillation (n=25). Support Vector Machine with Radial Basis Function (SVM-RBF) using multi-segment RR intervals was evaluated on Accuracy of PAF detection. The proposed Support Vector Machine with Radial Basis Function using multi-segment RR intervals achieved an accuracy of 99.17% for detecting paroxysmal atrial fibrillation.
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