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Anesthesia depth evaluation algorithm based on permutation and combination entropy

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Abstract

The quality of anesthesia is of great value to the risk of surgery and the recovery of postoperative patients. The evaluation of depth of anesthesia through EEG signals is an effective method to improve the quality of anesthesia. The difficulty of anesthesia depth evaluation lies in the lack of a relatively stable algorithm. In this study, the permutation and entropy algorithm are used as the system algorithm. Meanwhile, this study combines with the anesthesia depth assessment needs and actual conditions to improve the algorithm and realize the anesthesia depth detection function by extracting the feature parameters of different fields of EEG signals. In addition, this study analyzes multiple channels of EEG signals through adaptive neural networks to obtain quantified anesthesia depth values and compares this value with IoC monitors. Through experimental research, we can see that the method proposed in this paper has a certain practical effect.

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Author notes

  1. Wenwen Zhang and Tingting Yu make the equal contribution to this work and are co-first authors.

Authors and Affiliations

  1. Department of Anesthesiology, The First Hospital of Jilin University, Changchun, 130021, Jilin, People’s Republic of China

    Wenwen Zhang, Zongsheng Duan & Xinbai Li

  2. Department of Otolaryngology, Head and Neck Surgery, The First Hospital of Jilin University, Changchun, 130021, Jilin, People’s Republic of China

    Hong Yu & Tingting Yu

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  1. Wenwen Zhang
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  2. Hong Yu
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  3. Zongsheng Duan
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  4. Tingting Yu
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Correspondence to Xinbai Li.

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Zhang, W., Yu, H., Duan, Z. et al. Anesthesia depth evaluation algorithm based on permutation and combination entropy. Neural Comput & Applic 34, 6647–6660 (2022). https://doi.org/10.1007/s00521-021-06030-6

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  • DOI: https://doi.org/10.1007/s00521-021-06030-6

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