Hyun-Ju Lee, Dong-Kyoo Shin, Hee-Won Park, Soo-Han Kim, Dong-Il Shin, Journal of Internet Computing and Services, Vol. 12, No. 6, pp. 63-72, Dec. 2011
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Keywords: ECG, classifier, R-R interval, SVM, MLP, Random Forest, B-F Tree, Accuracy

Abstract

Cardiac disorder data are generally tested using the classifier and QRS-Complex and R-R interval which is used in this experiment are often extracted by ECG(Electrocardiogram) signals. The experimentation of ECG data with classifier is generally performed with SVM(Support Vector Machine) and MLP(Multilayer Perceptron) classifier, but this study experimented with Best-First Decision Tree(B-F Tree) derived from the Dicision Tree among Random Forest classifier algorithms to improve accuracy. To compare and analyze accuracy, experimentation of SVM, MLP, RBF(Radial Basic Function) Network and Decision Tree classifiers are performed and also compared the result of announced papers carried out under same interval and data. Comparing the accuracy of Random Forest classifier with above four ones, Random Forest is the best in accuracy. As though R-R interval was extracted using Band-pass filter in pre-processing of this experiment, in future, more filter study is needed to extract accurate interval.

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