基于CEEMDAN的脑组织氧信号处理算法及其应用OACSTPCD

Objective To propose a mathematical model for brain tissue oxygen monitoring based on three wavelengths.Methods Combined complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and permutation entropy(PE),the brain oxygen signal was processed.Meanwhile,the selection of PE value intervals was corrected by signal-to-noise ratio to improve cerebral oxygen noise reduction through adaptive filtering.Results In this paper,the data of three volunteers were collected and compared with the existing market equipment.The results indicated that compared to the empirical mode decomposition algorithm and ensemble empirical mode decomposition algorithm,the CEEMDAN algorithm proposed in this study had a root mean square error of less than 1.7 when compared to the reference equipment.The Pearson correlation coefficients for the three sets of data were 0.885,0.899,and 0.883 respectively,showing a high overall correlation(P<0.01).In hypoxia experiments,this algorithm could effectively monitor the trend of changes in brain oxygen values and demonstrated good practical value.Conclusion The algorithm effectively can remove baseline drift,low-frequency noise,and high-frequency noise,addressing mode mixing and residual noise issues and can enhance the accuracy of filtering,further improving the signal-to-noise ratio of the reconstructed signal and enhancing the effectiveness and stability of the system.