EEG entropy measures in anesthesia
Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of an...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-02-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00016/full |
| _version_ | 1819045744146907136 |
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| author | Zhenhu eLiang Yinghua eWang Yinghua eWang Xue eSun Duan eLi Logan James Voss Jamie eSleigh Satoshi eHagihira Xiaoli eLi Xiaoli eLi |
| author_facet | Zhenhu eLiang Yinghua eWang Yinghua eWang Xue eSun Duan eLi Logan James Voss Jamie eSleigh Satoshi eHagihira Xiaoli eLi Xiaoli eLi |
| author_sort | Zhenhu eLiang |
| collection | DOAJ |
| description | Objective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GA-BAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures (Shannon WE (SWE), Tsallis WE (TWE) and Renyi WE (RWE)), Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures (Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)). Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD) modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure.Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA. |
| first_indexed | 2024-12-21T10:33:26Z |
| format | Article |
| id | doaj.art-0bef63fab19b4a798d354e2a0a63f10b |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T10:33:26Z |
| publishDate | 2015-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-0bef63fab19b4a798d354e2a0a63f10b2022-12-21T19:07:08ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882015-02-01910.3389/fncom.2015.00016113307EEG entropy measures in anesthesiaZhenhu eLiang0Yinghua eWang1Yinghua eWang2Xue eSun3Duan eLi4Logan James Voss5Jamie eSleigh6Satoshi eHagihira7Xiaoli eLi8Xiaoli eLi9Institute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, ChinaState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, P.R. ChinaCenter for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, P.R. ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, ChinaInstitute of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaDepartment of Anesthesia, Waikato Hospital, Hamilton, New ZealandDepartment of Anesthesia, Waikato Hospital, Hamilton, New ZealandDepartment of Anesthesiology, Osaka University Graduate School of Medicine, Osaka, JapanState Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, P.R. ChinaCenter for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, P.R. ChinaObjective: Entropy algorithms have been widely used in analyzing EEG signals during anesthesia. However, a systematic comparison of these entropy algorithms in assessing anesthesia drugs’ effect is lacking. In this study, we compare the capability of twelve entropy indices for monitoring depth of anesthesia (DoA) and detecting the burst suppression pattern (BSP), in anesthesia induced by GA-BAergic agents.Methods: Twelve indices were investigated, namely Response Entropy (RE) and State entropy (SE), three wavelet entropy (WE) measures (Shannon WE (SWE), Tsallis WE (TWE) and Renyi WE (RWE)), Hilbert-Huang spectral entropy (HHSE), approximate entropy (ApEn), sample entropy (SampEn), Fuzzy entropy, and three permutation entropy (PE) measures (Shannon PE (SPE), Tsallis PE (TPE) and Renyi PE (RPE)). Two EEG data sets from sevoflurane-induced and isoflu-rane-induced anesthesia respectively were selected to assess the capability of each entropy index in DoA monitoring and BSP detection. To validate the effectiveness of these entropy algorithms, phar-macokinetic / pharmacodynamic (PK/PD) modeling and prediction probability analysis were applied. The multifractal detrended fluctuation analysis (MDFA) as a non-entropy measure was compared.Results: All the entropy and MDFA indices could track the changes in EEG pattern during different anesthesia states. Three PE measures outperformed the other entropy indices, with less baseline vari-ability, higher coefficient of determination and prediction probability, and RPE performed best; ApEn and SampEn discriminated BSP best. Additionally, these entropy measures showed an ad-vantage in computation efficiency compared with MDFA.Conclusion: Each entropy index has its advantages and disadvantages in estimating DoA. Overall, it is suggested that the RPE index was a superior measure.Significance: Investigating the advantages and disadvantages of these entropy indices could help improve current clinical indices for monitoring DoA.http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00016/fullAnesthesiaEEGentropypharmacokinetic/pharmacodynamic modelingdepth of anesthesia monitoring |
| spellingShingle | Zhenhu eLiang Yinghua eWang Yinghua eWang Xue eSun Duan eLi Logan James Voss Jamie eSleigh Satoshi eHagihira Xiaoli eLi Xiaoli eLi EEG entropy measures in anesthesia Frontiers in Computational Neuroscience Anesthesia EEG entropy pharmacokinetic/pharmacodynamic modeling depth of anesthesia monitoring |
| title | EEG entropy measures in anesthesia |
| title_full | EEG entropy measures in anesthesia |
| title_fullStr | EEG entropy measures in anesthesia |
| title_full_unstemmed | EEG entropy measures in anesthesia |
| title_short | EEG entropy measures in anesthesia |
| title_sort | eeg entropy measures in anesthesia |
| topic | Anesthesia EEG entropy pharmacokinetic/pharmacodynamic modeling depth of anesthesia monitoring |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00016/full |
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