Analysis of Anesthesia Characteristic Parameters Based on the EEG Signal_Journal of Biomedical Engineering

Authors：

WANGFeng ¹ ,  LIXiaoou ^1,2

1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Shanghai Medical Instrumentation College, Shanghai 200093, China;

Corresponding author：

LIXiaoou, Email: lixo@smic.edu.cn

Keywords：

anesthesia electroencephalogram signals; wavelet transform; independent component analysis; complexity; entropy

DOI：

10.7507/1001-5515.20150003

Video：

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Abstract Full text Figures/Tables Video References Cited by

All the collected original electroencephalograph (EEG) signals were the subjects to low-frequency and spike noise. According to this fact, we in this study performed denoising based on the combination of wavelet transform and independent component analysis (ICA). Then we used three characteristic parameters, complexity, approximate entropy and wavelet entropy values, to calculate the preprocessed EEG data. We then made a distinguishing judge on the EEG state by the state change rate of the characteristic parameters. Through the anesthesia and non-anesthesia EEG data processing results showed that each of the three state change rates could reach about 50.5%, 21.6%, 19.5%, respectively, in which the performance of wavelet entropy was the highest. All of them could be used as a foundation in the quantified research of depth of anesthesia based on EEG analysis.

Citation： WANGFeng, LIXiaoou. Analysis of Anesthesia Characteristic Parameters Based on the EEG Signal. Journal of Biomedical Engineering, 2015, 32(1): 13-18, 31. doi: 10.7507/1001-5515.20150003 Copy

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1. 贾爱宾, 王敏, 刘法胜, 等.脑电信号的小波变换去噪算法——基于中值的尺度相关阈值法[J].生物医学工程学杂志, 2009, 26(6):1227-1229.
2. 谢松云, 张振中, 张伟平, 等.基于ICA的脑电信号去噪方法研究与应用[J].中国医学影像技术, 2007, 23(10):1562-1565.
3. 文莉, 刘正士, 葛运建.小波去噪的几种方法[J].合肥工业大学学报(自然科学版), 2002, 25(2):167-172.
4. 吴小培, 冯焕清, 周荷琴, 等.基于小波变换的脑电信号噪声消除方法[J].电路与系统学报, 2000, 5(3):96-98.
5. 谢松云, 潘辉, 张伟平.结合小波包和ICA的脑电信号特征波提取方法[J].计算机应用研究, 2008, 25(9):2671-2673.
6. 李敏.基于模糊神经网络麻醉深度监测的研究[D].杭州:浙江大学, 2006.
7. 张烈平, 莫玮.基于小波变换的麻醉监测诱发脑电信号分析的研究[J].昆明理工大学学报:理工版, 2003, 28(3):97-100.
8. 何庆华, 廖维宏, 吴宝明, 等.脑电分析在麻醉深度监测中的应用[J].医疗卫生装备, 2008, 29(4):30-31, 34.
9. 张玉新, 滕桂法, 赵洋, 等.基于小波变换模极大值的信号去噪方法研究[J].河北农业大学学报, 2009, 32(1):114-116.
10. 张引红, 李全禄.基于小波熵的鼾音信号去噪处理[J].计算机工程与应用, 2011, 47(30):19-22.
11. 杨仁桓, 宋爱国, 徐宝国.基于谐波小波包变换的脑电波基本节律分析[J].东南大学学报(自然科学版), 2008, 38(6):996-999.
12. KUMAR Y, DEWAL M L, ANAND R S. Relative wavelet energy and wavelet entropy based epileptic brain signals classification[J]. Biomed Eng Lett, 2012, 2(3): 147-157.
13. 陈晓平, 和卫星, 温军玲.基于脑电波复杂度的麻醉深度监测[J].江苏大学学报(自然科学版), 2003, 24(6):73-75.
14. ZHANG L Y, ZHENG C X. Lempel-Ziv complexity changes and physiological mental fatigue level during different mental fatigue state with spontaneous EEG[J]. Health, 2009, 1(1): 35-38.
15. 徐进, 郑崇勋, 和卫星.基于脑电近似熵分析的麻醉深度监测研究[J].航天医学与医学工程, 2004, 17(3):205-209.
16. VAVADI H, AYATOLLAHI A, MIRZAEI A. A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands[J]. J Biomed Sci Eng, 2010, 3(12): 1182-1189.

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