An EEMD-based method for removing residual blood oxygen signal noise by combining wavelet and singular spectrum analysis

Abstract

Blood oxygen content plays an important role in clinical diagnosis and treatment. However, interference signals will be introduced in the process of the signal acquisition, which will affect the accuracy of measurement results. Although the filter can eliminate most of the interference, there will still be a part of the residual interference signal. In order to eliminate these residual interference, this paper proposes a signal denoising method based on ensemble empirical mode decomposition (EEMD), lifting wavelet transform (LWT) and singular spectrum analysis (SSA). Firstly, the measured signal is decomposed by EEMD to obtain multiple modal components (IMF). The high-frequency components are denoised by LWT, and then reconstructed with the low-frequency components. Finally, the SSA is carried out on the reconstructed signal, the appropriate components are selected to reconstruct the signal to obtain the final denoised signal. After testing the EEMD-LWT-SSA method with simulated and measured signals, it can be seen that the method can effectively suppress the residual noise in signals and improve the accuracy of data.