Comparative study between EMD, EEMD, and CEEMDAN based on De-Noising Bioelectric Signals

Abstract

In synch with the artificial intelligence era and particularly in the biomedical field, biomedical signals like electrocardiographic (ECG), electromyographic (EMG), and Electroencephalogram (EEG) are being used in various applications, such as artificial hand and arterial pressure. However, identifying a patient's ailment is still a challenge. In this paper, we have utilized three empirical mode decomposition techniques to minimize the impact of additive noises on noninvasive biomedical signals. These methods are the classical empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and the complete ensemble empirical mode decomposition with additive noise (CEEMDAN). Using the correlation coefficient, we conducted an extensive simulation and detailed comparative study between the noisy and reconstructed signals. The results show that the CEEMDAN method is the most effective in reducing noise compared to the other two methods.