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KMID : 1137820180390020069
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의공학회지
2018 Volume.39 No. 2 p.69 ~ p.79
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Prediction of Paroxysmal Atrial Fibrillation using Time-domainalysis and Random Forestv
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Lee Seung-Hwan
Lee Kyoung-Joung
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Abstract
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The present study proposes an algorithm that can discriminate between normal subjects and paroxysmal atrial fibrillation (PAF) patients, which is conducted using electrocardiogram (ECG) without PAF events. For this, time-domain features and random forest classifier are used. Time-domain features are obtained from Poincare plot, Lorenz plot of ?RR interval, and morphology analysis. Afterward, three features are selected in total through feature selection. PAF patients and normal subjects are classified using random forest. The classification result showed that sensitivity and specificity were 81.82% and 95.24% respectively, the positive predictive value and negative predictive value were 96.43% and 76.92% respectively, and accuracy was 87.04%. The proposed algorithm had an advantage in terms of the computation requirement compared to existing algorithm, so it has suggested applicability in the more efficient prediction of PAF.
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KEYWORD
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atrial fibrillation prediction, random forest, Lorenz plot, Poincare plot
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