基于ICEEMDAN与样本熵的脑血氧信号去噪方法

doi:10.16180/j.cnki.issn1007-7820.2024.06.006

Abstract

Abstract:

Both human physiological activity and random noise have an impact on the accuracy of cerebral blood oxygen measurement data. It is necessary to solve some noise interference encountered during signal acquisition to improve the measurement accuracy of cerebral blood oxygen detection. This study proposes a denoising method of cerebral oximetry signals by combining ICEEMDAN(Improved Complete Empirical Mode Decomposition with Adaptive Noise) and SampEn(Sample Entropy). Specifically, the modal decomposition of the cerebral blood oxygen signal is performed using ICEEMDAN to obtain the IMF components with different time complexity. The time complexity of each IMF(Intrinsic Mode Function) component is judged by the sample entropy value, and the appropriate component is selected to reconstruct the signal according to the sample entropy value of each IMF component, thus realizing the noise removal of the original signal. Experimental results show that the proposed method can effectively remove the original cerebral blood oxygen signal and realize the accuracy improvement of the collected data, thus improving the accuracy of cerebral blood oxygen detection.

Key words: cerebral blood oxygen, accuracy, ICEEMDAN, sample entropy, intrinsic mode function, reconstructed signal, blood oxygen signal, noise removal

CLC Number:

TN911.7

CAO Yan, ZHAO Bin, XING Zhiming, JIN Zihao, DONG Xiangmei, GAO Xiumin. Denoising Method of Cerebral Blood Oxygen Signal Based on ICEEMDAN and Sample Entropy[J].Electronic Science and Technology, 2024, 37(6): 44-50.

Figures/Tables 12

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References 24

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IMF	样本熵(SampEn)
1	0.037 9
2	0.444 3
3	0.427 8

IMF	SampEn-HbO₂	SampEn-HHb
1	1.734 0	1.474 5
2	1.239 0	1.193 0
3	0.316 4	0.697 0
4	0.241 4	0.574 6
5	0.194 6	0.494 4
6	0.082 4	0.254 6
7	0.029 4	0.078 5

Denoising Method of Cerebral Blood Oxygen Signal Based on ICEEMDAN and Sample Entropy

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