Evaluation of bagging ensemble method with time-domain feature extraction for diagnosing of arrhythmia beats

Abstract

We explore the effect of using bagged decision tree (BDT) as an ensemble learning method with proposed time-domain feature extraction methods on electrocardiogram (ECG) arrhythmia beat classification comparing with single decision tree (DT) classifier. RR interval is the main property which defines irregular heart rhythm, and its ratio to the previous value and difference from mean value are used as morphological feature extraction methods. Form factor, its ratio to the previous value and difference from mean value are used to express ECG waveform complexity. In addition, skewness and second-order linear predictive coding coefficients are added to the feature vector of 56,569 ECG heart beats obtained from MIT–BIH arrhythmia database as time-domain feature extraction methods. The quarter of ECG heart beat samples are used as test data for DT and BDT. The performance measures of these classifiers are evaluated using the metrics such as accuracy, sensitivity, specificity and Kappa coefficient for both classifiers, and the performance of BDT classifier is examined for number of base learners up to 75. The BDT results in more predictive performance than DT according to the performance measures. BDT with 69 base learners has 99.51 % of accuracy, 97.50 % of sensitivity, 99.80 % of specificity and 0.989 of Kappa coefficient while DT gives 98.78, 96.05, 99.57 and 0.975 %, respectively. These metrics show that the suggested BDT increases the numbers of successfully identified arrhythmia beats. Moreover, BDT with at least three base learners has higher distinguishing capability than DT.