Research on analysis method of multi-fractal de-trended fluctuation of electroencephalogram focus on mental stress evaluation_Journal of Biomedical Engineering

Authors：

 LI Xin ^1,2,3 , SUN Xiaoqi ^1,2 , QI Xiaoying ^1,2 , HOU Yongjie ⁴ , TIAN Yanxiu ^1,2

1. Institute of Biomedical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, P.R.China;
2. Measurement Technology and Instrumentation Key Lab of Hebei Province, Qinhuangdao, Hebei 066004, P.R.China;
3. The College of Life Science and Bio-Engineering, Beijing University of Technology, Beijing 100124, P.R.China;
4. Qinhuangdao Prospect Photoeletric Tech Co.,Ltd, Qinhuangdao, Hebei 066004, P.R.China;

Corresponding author：

LI Xin, Email: yddylixin@ysu.edu.cn

Keywords：

electroencephalogram; multi-fractal de-trended fluctuation analysis; singular spectrum width; singular index

DOI：

10.7507/1001-5515.201512028

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The multi-fractal de-trended fluctuation analysis was used to estimate the mental stress in the present study. In order to obtain the optimal fractal order of the multi-fractal de-trended fluctuation analysis, we analyzed the relationship between singular index and Hurst index with order. We recorded the electroencephalogram (EEG) of 14 students, compared the relationship between singular index, Hurst index and quality index, ensured the optimal order being [—5, 5] and achieved the estimation of mental stress with the β wave in the EEGs. The result indicated that Hurst index and quality index of the EEGs under mental stress were greater than those of EEGs in the relaxing state. The Hurst index was gradually decreasing with the order increasing and was finally approaching a constant, while the quality index was amplified and variation of amplitude of the singular index was more obvious. We also compared the amplitude and the width of singular spectrum of the EEGs under the two conditions, and results indicated that the characteristics of multi-fractal spectrum of the EEGs under different conditions were different, namely the width of singular spectrum of the EEGs under mental stress was greater than that under relax condition.

Citation： LI Xin, SUN Xiaoqi, QI Xiaoying, HOU Yongjie, TIAN Yanxiu. Research on analysis method of multi-fractal de-trended fluctuation of electroencephalogram focus on mental stress evaluation. Journal of Biomedical Engineering, 2017, 34(2): 180-187. doi: 10.7507/1001-5515.201512028 Copy

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2.	Peng C K, Buldyrev S V, Havlin S, et al. Mosaic organization of DNA nucleotides. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, 1994, 49(2): 1685-1689.
3.	Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A, 2002, 316(1/4): 87-114.
4.	邹鸣, 高庸, 王新猛, 等. 脑电信号的多重分形去趋势波动分析. 北京生物医学工程, 2013, 32(3): 226-229.
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7.	Marton L F, Brassai S T, Bako L. et al., Detrended fluctuation analysis of EEG signals. Procedia Technology, 2014, 12(1): 125-132.
8.	李红红. 脑电信号数据分析方法及其在压力情感状态评估中的应用. 秦皇岛: 燕山大学, 2014.
9.	Mandelbrot B B. van Ness J W., Fractional brownian motion, fractional noise and applications. SIAM Review, 1968, 10(4): 422-437.

1. Halsey T C., Jensen M H, Kadanoff L P, et al. Fractal measures and their singularities: The characterization of strange sets. Phys Rev A, 1986, 33(2): 1141-1151.
2. Peng C K, Buldyrev S V, Havlin S, et al. Mosaic organization of DNA nucleotides. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, 1994, 49(2): 1685-1689.
3. Kantelhardt J W, Zschiegner S A, Koscielny-Bunde E, et al. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A, 2002, 316(1/4): 87-114.
4. 邹鸣, 高庸, 王新猛, 等. 脑电信号的多重分形去趋势波动分析. 北京生物医学工程, 2013, 32(3): 226-229.
5. Bachmann M, Suhhova A, Jass J. et al. Detrended fluctuation analysis of EEG in depression. IFMBE Proc, 2014, 41(1): 694-697.
6. Dutta S, Ghosh D, Samanta S. et al. Multifractal parameters as an indication of different physiological and pathological states of the human brain. Physica A: Statistical Mechanics and its Applications, 2014, 396(15): 155-163.
7. Marton L F, Brassai S T, Bako L. et al., Detrended fluctuation analysis of EEG signals. Procedia Technology, 2014, 12(1): 125-132.
8. 李红红. 脑电信号数据分析方法及其在压力情感状态评估中的应用. 秦皇岛: 燕山大学, 2014.
9. Mandelbrot B B. van Ness J W., Fractional brownian motion, fractional noise and applications. SIAM Review, 1968, 10(4): 422-437.

Journal of Biomedical Engineering

Research on analysis method of multi-fractal de-trended fluctuation of electroencephalogram focus on mental stress evaluation

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