Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier

摘要

Highlights•Classification of AAMI recommended ECG heartbeats is addressed.•HOS and entropy features are computed from the modes of ICEEMD.•Successfully addressed class imbalance issue using data level resampling techniques.•AdaBoost ensemble classifier used for classification.•Obtained classification accuracy greater than 99%.AbstractComputer-aided heartbeat classification has a significant role in the diagnosis of cardiac dysfunction. Electrocardiogram (ECG) provides vital information about the heartbeats. In this work, we propose a method for classifying five groups of heartbeats recommended by AAMI standard EC57:1998. Considering the nature of ECG signal, we employed a non-stationary and nonlinear decomposition technique termed as improved complete ensemble empirical mode decomposition (ICEEMD). Later, higher order statistics and sample entropy measures are computed from the intrinsic mode functions (IMFs) obtained from ICEEMD on each ECG segment. Furthermore, three data level pre-processing techniques are performed on the extracted feature set, to balance the distribution of heartbeat classes. Finally, these features fed to AdaBoost ensemble classifier for discriminating the heartbeats. Simulation results show that the proposed method provides a better solution to the class imbalance problem in heartbeat classification.