Journal of Medical and Biological Engineering

, Volume 37, Issue 2, pp 171–180 | Cite as

Monitoring Depth of Anesthesia Using Detrended Fluctuation Analysis Based on EEG Signals

  • Xiaoou LiEmail author
  • Feng Wang
  • Guilong Wu
Original Article


Detrended fluctuation analysis (DFA) is appropriate for the analysis of long-range correlation in nonstationary time series. In this study, DFA was used to study electroencephalography (EEG) fluctuations in order to assess the depth of anesthesia (DOA) and measure the level of consciousness. The fluctuation function F(s) was calculated. The distribution of F(s) in segments was used to classify anesthesia state levels into awake, light, moderate, and deep states. A linear fit of F(s) versus s in each segment was performed. Finally, the point at which the fitted line crossed the defined line divided the four zones corresponding to the four states of DOA from 100 to 0 in the coordinate system. Experimental results demonstrate that the proposed method can accurately identify the states of DOA based on EEG signals. The ranges of DOA values can be extended through adjustable parameters, improving the adaptability of the algorithm. The results are close to the bispectral index values, which can be used to identify anesthesia states. The proposed DFA method is effective for monitoring DOA.


Depth of anesthesia Detrended fluctuation analysis Fluctuation function Wavelet transform Electroencephalogram (EEG) 



This study was funded by the Shanghai Municipal Education Commission Project (Grant No. 12YZ194), Shanghai Science Technology Research Project (Grant No. 11441902400), and Natural Science Foundation of Shanghai (Grant No. 14ZR1440100). The authors would like to thank NATION Corporation and the Fifth People’s Hospital Affiliated to Fudan University for providing the EEG instrument and clinical patients, respectively.

Compliance with Ethical Standards

Conflicts of interest



  1. 1.
    Zoughi, T., Boostani, R., & Deypir, M. (2012). A wavelet-based estimating depth of anesthesia. Engineering Applications of Artificial Intelligence, 25, 1710–1722.CrossRefGoogle Scholar
  2. 2.
    Plusquellec, P., & Bousquet, L. (2007). Time-delay for two-compartment models used for study of enterohepatic circulation of drugs. IEEE Transactions on Biomedical Engineering, 31, 469–472.Google Scholar
  3. 3.
    Pandit, J. J., Cook, T. M., & O’Sullivan, E. (2013). A national survey of anesthetists (NAP5 Baseline) to estimate an annual incidence of accidental awareness during general anesthesia in the UK. Anesthesia, 68, 343–353.CrossRefGoogle Scholar
  4. 4.
    Wang, L., Ni, Z. Q., Meng, J., Qiu, F., & Huang, J. (2011). A general method for calculation of depth of anesthesia. Procedia Environmental Sciences, 8, 209–214.CrossRefGoogle Scholar
  5. 5.
    Tan, Z. B. (2007). Monitoring the depth of anesthesia: Methods based on EEG signal processing. Wayne State University Thesis.Google Scholar
  6. 6.
    Nguyen-Ky, T., Wen, P., & Li, Y. (2009). Theoretical basis for identification of different anesthetic states based on routinely recorded EEG during operation. Computers in Biology and Medicine, 39, 40–45.CrossRefGoogle Scholar
  7. 7.
    Jospin, M., Caminal, P., Jensen, E. W., Litvan, H., Vallverdu, M., Struys, M. M. R. F., et al. (2007). Detrended fluctuation analysis of EEG as a measure of depth of anesthesia. IEEE Transactions on Biomedical Engineering, 54(5), 840–846.CrossRefGoogle Scholar
  8. 8.
    Li, X. X., Li, D., Liang, Z., Voss, L. J., & Sleigh, J. W. (2008). Analysis of depth of anesthesia with Hilbert–Huang spectral entropy. Clinical Neurophysiology, 19, 2465–2475.CrossRefGoogle Scholar
  9. 9.
    Sebel, P. S., Bowdle, T. A., Ghoneim, M. M., Rampil, I. J., Padilla, R. E., Gan, T. J., et al. (2004). The incidence of awareness during anesthesia: A multicenter United States study. Anesthesia and Analgesia, 99, 833–839.CrossRefGoogle Scholar
  10. 10.
    Belkacem, A. N., Hirose, H., Yoshimura, N., Shin, D., & Koike, Y. (2014). Classification of four eye directions from EEG signals for eye-movement-based communication systems. Journal of Medical and Biological Engineering, 34(6), 581–588.Google Scholar
  11. 11.
    Ni, Z. Q., Wang, L., Meng, J., Qiu, F., & Huang, J. (2011). EEG signal processing in anesthesia: Feature extraction of time and frequency parameters. Procedia Environmental Sciences, 8, 215–220.CrossRefGoogle Scholar
  12. 12.
    Voss, L., & Sleigh, J. (2007). Monitoring consciousness: The current status of EEG-based depth of anesthesia monitors. Best Practice and Research Clinical Anesthesiology, 21(3), 313–325.CrossRefGoogle Scholar
  13. 13.
    Knorr, B. R., McGrath, S. P., & Blike, G. T. (2006). Using a generalized neural network to identify airway obstructions in anesthetized patients post-operatively based on photoplethysmography. In Proceeding of IEEE EMBS annual international conference, 2006 (pp. 6765–6768).Google Scholar
  14. 14.
    Johansen, J. W., & Sebel, P. S. (2000). Development and clinical application of electroencephalographic bispectrum monitoring. Anesthesiology, 93, 1336–1344.CrossRefGoogle Scholar
  15. 15.
    Nguyen-Ky, T., Wen, P., & Li, Y. (2010). An improved detrended moving-average method for monitoring the depth of anesthesia. IEEE Transactions on Biomedical Engineering, 57(10), 2369–2378.CrossRefGoogle Scholar
  16. 16.
    Kelly, S. D. (2007). Monitoring consciousness using the Bispectrum Index during anesthesia, a pocket guide for clinicians (pp. 1–40). Covidien.Google Scholar
  17. 17.
    Bruhn, J., Myles, P. S., Sneyd, R., & Struys, M. M. R. F. (2006). Depth of anesthesia monitoring: What’s available, what’s validated and what’s next? British Journal of Anesthesia, 97(1), 85–94.CrossRefGoogle Scholar
  18. 18.
    Rezek, I., Roberts, S. J., & Conradt, R. (2007). Increasing the depth of anesthesia assessment. IEEE Engineering in Medicine and Biology Magazine, 26(2), 64–73.CrossRefGoogle Scholar
  19. 19.
    Ferents, R., Lipping, T., Anier, A., Jntti, V., Melto, S., & Hovilehto, S. (2006). Comparison of entropy and complexity measures for the assessment of depth of a sedation. IEEE Transactions on Biomedical Engineering, 53(6), 1067–1077.CrossRefGoogle Scholar
  20. 20.
    Anier, A., Lipping, T., Melto, S., & Hovilehto, S. (2004). Higuchi fractal dimension and spectral entropy as measures of depth of sedation in intensive care unit. In Proceeding of the 26th annual international conference of IEEE EMBS, 2004 (pp. 526–529).Google Scholar
  21. 21.
    Koskine, M., Seppanen, T., & Tong, S. B. (2006). Monotonicity of approximate entropy during transition from awareness to unresponsiveness due to propofol anesthesia induction. IEEE Transactions on Biomedical Engineering, 53(4), 669–675.CrossRefGoogle Scholar
  22. 22.
    Ghanatbari, M., Mehridehnavi, A. R., Rabbani, H., Mahoori, A. R., & Mehrjoo, M. (2010). A comparative study of the output correlations between wavelet transform, neural and neuro fuzzy networks and BIS index for depth of anesthesia. In IEEE symposium on industrial electronics and applications, 2010 (pp. 655–659).Google Scholar
  23. 23.
    Taslimi, P., Rabiee, H. R., & Shakouri, G. (2009). An empirical centre assignment in RBF network for quantification of anesthesia using wavelet-domain features. In Proceedings of the 4th international IEEE EMBS conference on neural engineering, 2009 (pp. 510–513).Google Scholar
  24. 24.
    Ortolani, O., Conti, A., Filippo, A. D., Adembri, C., Moraldi, E., Evangelisti, A., et al. (2002). EEG signal processing in anesthesia: Use of a neural network technique for monitoring depth of anesthesia. British Journal of Anesthesia, 88(5), 644–648.CrossRefGoogle Scholar
  25. 25.
    Nguyen-Ky, T., Wen, P., Li, Y., & Gray, R. (2010). De-noising a raw EEG signal and measuring depth of anesthesia for general anesthesia patients. In IEEE/ICME international conference on complex medical engineering, 2010 (pp. 254–259).Google Scholar
  26. 26.
    Nguyen-Ky, T., Wen, P., Li, Y., & Gray, R. (2011). Measuring and reflecting depth of anesthesia using wavelet and power spectral density. IEEE Transactions on Information Technology in Biomedicine, 15(4), 630–639.CrossRefGoogle Scholar
  27. 27.
    Zikov, T., Bibian, S., Dumont, G. A., Huzmezan, M., & Ries, C. R. (2006). Quantifying cortical activity during general anesthesia using wavelet analysis. IEEE Transactions on Biomedical Engineering, 53(4), 71–81.CrossRefGoogle Scholar
  28. 28.
    Zhang, X. S., Roy, R. J., & Jensen, E. W. (2011). EEG complexity as a measure of depth of anesthesia for patients. IEEE Transactions on Biomedical Engineering, 48, 1424–1433.CrossRefGoogle Scholar
  29. 29.
    Accardo, A., Cusenza, M., & Monti, F. (2009). Linear and non-linear parameterization of EEG during monitoring of carotid endarterectomy. Computers in Biology and Medicine, 39, 512–518.CrossRefGoogle Scholar
  30. 30.
    Guo, L., Wu, Y. X., Zhao, L., Cao, T., Yan, W. L., & Shen, X. Q. (2011). Classification of mental task from EEG signals using immune feature weighted support vector machines. IEEE Transactions on Magnetics, 47(5), 866–869.CrossRefGoogle Scholar
  31. 31.
    Vavadi, H., Ayatollahi, A., & Mirzaei, A. (2010). A wavelet-approximate entropy method for epileptic activity detection from EEG and its sub-bands. Journal of Biomedical Science and Engineering, 3, 1182–1189.CrossRefGoogle Scholar
  32. 32.
    Nguyen-Ky, T., Wen, P., & Li, Y. (2010). Improving the accuracy of depth of anesthesia using modified detrended fluctuation analysis method. Biomedical Signal Processing and Control, 5, 59–65.CrossRefGoogle Scholar
  33. 33.
    Chen, S. C., See, A. R., Hou, C. J., Chen, Y. J., Liang, C. K., Hou, P. Y., et al. (2014). Coherence validation of alternative sleep EEG electrode placements using wavelet transform. Journal of Medical and Biological Engineering, 34(6), 528–534.Google Scholar
  34. 34.
    Nguyen-Ky, T., Wen, P., Li, Y., & Malan, M. (2012). Measuring the hypnotic depth of anesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques. Computers in Biology and Medicine, 42, 680–691.CrossRefGoogle Scholar
  35. 35.
    Poornachandra, S. (2008). Wavelet-based denoising using subband dependent threshold for EEG signals. Digital Signal Processing, 1, 49–55.CrossRefGoogle Scholar
  36. 36.
    Zhu, J. F., & Huang, Y. D. (2013). Improved threshold function of wavelet domain signal de-noising. In Proceedings of the 2013 international conference on wavelet analysis and pattern recognition, 2013 (pp. 190–195).Google Scholar
  37. 37.
    Donoho, D. L. (1995). De-noising via soft-thresholding. IEEE Transactions on Information Theory, 41(3), 613–627.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Chen, J., & Li, G. Q. (2014). Tsallis wavelet entropy and its application in power signal analysis. Entropy, 16, 3009–3025.CrossRefGoogle Scholar
  39. 39.
    Peng, C. K., Buldyrev, S. V., Goldberger, A. L., Mantegna, R. N., Peng, C. K., Simons, M., et al. (1995). Statistical properties of DNA sequences. Physica A, 221, 180–192.CrossRefzbMATHGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  1. 1.College of Medical InstrumentsShanghai University of Medicine & Health SciencesShanghaiChina
  2. 2.School of Medical Instrument and Food EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  3. 3.Department of AnesthesiologyThe Fifth People’s Hospital Affiliated to Fudan UniversityShanghaiChina

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