An Optimized Baseline Wander Removal Algorithm Based on Ensemble Empirical Mode Decomposition

摘要

This paper proposes a novel optimized framework for baseline wander removal from the ECG signal based on Ensemble Empirical Mode Decomposition (EEMD). Computational complexity of the algorithm is one of the main factors which affects the real time monitoring of cardio activities and diagnosis of arrhythmias. The computational complexity in EEMD is significantly reduced by using the Empirical Mode Decomposition (EMD) as a pre-processing stage. Empirical Mode Decomposition decomposes the noisy ECG signal into intrinsic mode functions (IMF). The IMF components which are affected by baseline wander noise are automatically identified based on the modified Ratio of the Zero Crossing Number (RZCN) parameter. EEMD is used further to decompose only the noisy IMF components. This proposed method is evaluated using real-time ECG signals which are available at MIT-BIH arrhythmia database and simulated ECG signals using MATLAB functions. The computational efficiency of this proposed method is measured using MATLAB profiling functions, and the proposed method is compared to traditional EMD and EEMD based baseline wander removal methods. Signal to Noise Ratio (SNR), correlation coefficient (CCR) and Root Mean Square Error (RMSE) parameters are used to compare the performance of the proposed method with traditional EMD and EEMD based methods. Results show that the proposed method performs better than traditional EMD and EEMD based methods, and it is computationally more efficient than EEMD.