Adaptive ECG beat classification by ordinal pattern based entropies

Abstract

In this paper, we investigate the applicability of the permutation entropy (PE) and the conditional entropy of ordinal patterns (CEOP) to Electrocardiogram (ECG) data analysis. We define a signal dependent threshold based on the PE and the CEOP for the detection of abnormal ECG beats. Parameters of the proposed threshold formula are calibrated using the MIT-BIH Arrhythmia and the European Society of Cardiology ST-T (ESC) databases. The experimental results show that the difference between CEOP and PE is marginal, and that the algorithm is less sensitive to the parameter setting. We achieved a classification rate of 93.62% in the case of the MIT-BIH database, and 99.57% in the case of the ESC database. Although these algorithms still need to be improved, the above results for the ESC database confirm that ordinal pattern based entropies are promising for ECG beat classification.