Research on the modified algorithm for improving accuracy of Random Forest classifier which identifies automatically arrhythmia


KIPS Transactions on Computer and Communication Systems, Vol. 18, No. 6, pp. 341-348, Dec. 2011
10.3745/KIPSTB.2011.18.6.341,   PDF Download:

Abstract

ECG(Electrocardiogram), a field of Bio-signal, is generally experimented with classification algorithms most of which are SVM(Support Vector Machine), MLP(Multilayer Perceptron). But this study modified the Random Forest Algorithm along the basis of signal characteristics and comparatively analyzed the accuracies of modified algorithm with those of SVM and MLP to prove the ability of modified algorithm. The R-R interval extracted from ECG is used in this study and the results of established researches which experimented co-equal data are also comparatively analyzed. As a result, modified RF Classifier showed better consequences than SVM classifier, MLP classifier and other researches` results in accuracy category. The Band-pass filter is used to extract R-R interval in pre-processing stage. However, the Wavelet transform, median filter, and finite impulse response filter in addition to Band-pass filter are often used in experiment of ECG. After this study, selection of the filters efficiently deleting the baseline wandering in pre-processing stage and study of the methods correctly extracting the R-R interval are needed.


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Cite this article
[IEEE Style]
H. J. Lee, D. K. Shin, H. W. Park, S. H. Kim and D. I. Shin, "Research on the modified algorithm for improving accuracy of Random Forest classifier which identifies automatically arrhythmia," KIPS Journal B (2001 ~ 2012) , vol. 18, no. 6, pp. 341-348, 2011. DOI: 10.3745/KIPSTB.2011.18.6.341.

[ACM Style]
Hyun Ju Lee, Dong Kyoo Shin, Hee Won Park, Soo Han Kim, and Dong Il Shin. 2011. Research on the modified algorithm for improving accuracy of Random Forest classifier which identifies automatically arrhythmia. KIPS Journal B (2001 ~ 2012) , 18, 6, (2011), 341-348. DOI: 10.3745/KIPSTB.2011.18.6.341.