This study presents a new method to apply the interval second-order difference plot (ISODP) and interval permutation entropy (IPE) techniques to assess the depth of anaesthesia (DoA). First, the denoised electroencephalograph signals are decomposed into 13 different frequency bands. The second-order difference plot (SODP) and permutation entropy (PE) values of each frequency band are calculated. The SODP and PE values of high-frequency bands (21.5-47 Hz) show the highest linear relationship with the anaesthesia states, therefore they are selected to form the parameter set. Then the SODP and PE parameters are fine tuned using the interval feature technique. Finally, a new index is designed using the ISODP and IPE. The new index is evaluated and compared with measured bispectral (BIS). The results show that there is a very close correlation between the proposed index and the BIS during different anaesthetic states. The new index also shows an earlier time response (3.1–59.7 s) than BIS during the change of anaesthetic states. In addition, the proposed index is able to continuously assess the DoA of patients when the quality of signal is poor and the BIS does not have any valid outputs.

References

1. 1)
  - 19. Olofsen, E., Sleigh, J., Dahan, A.: ‘Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect’, Br. J. Anaesth., 2008, 101, (6), pp. 810–821.
2. 2)
  - 17. Kuizenga, K., Wierda, J., Kalkman, C.: ‘Biphasic EEG changes in relation to loss of consciousness during induction with thiopental, propofol, etomidate, midazolam or sevoflurane’, Br. J. Anaesth., 2001, 86, (3), pp. 354–360.
3. 3)
  - 21. Silva, A., Campos, S., Monteiro, J., et al: ‘Performance of anesthetic depth indexes in rabbits under propofol anesthesia: prediction probabilities and concentration-effect relations’, Anesthesiology, 2011, 115, (2), pp. 303–314.
4. 4)
  - 27. Kim, J., Hyub, H., Yoon, S.Z., et al: ‘Analysis of EEG to quantify depth of anesthesia using hidden Markov model’, in Babiloni, F., Cerutti, S. (Eds.): ‘Analysis of EEG to quantify depth of anesthesia using hidden Markov model’ (IEEE, 2014), pp. 4575–4578.
5. 5)
  - 8. Chen, D., Li, D., Xiong, M., et al: ‘GPGPU-aided ensemble empirical-mode decomposition for EEG analysis during anesthesia’, IEEE Trans. Inf. Technol. Biomed., 2010, 14, (6), pp. 1417–1427.
6. 6)
  - 7. Li, X., Li, D., Liang, Z., et al: ‘Analysis of depth of anesthesia with Hilbert–Huang spectral entropy’, Clin. Neurophysiol., 2008, 119, (11), pp. 2465–2475.
7. 7)
  - 10. Nguyen-Ky, T., Wen, P., Li, Y., et al: ‘Measuring and reflecting depth of anesthesia using wavelet and power spectral density’, IEEE Trans. Inf. Technol. Biomed., 2011, 15, (4), pp. 630–639.
8. 8)
  - 12. Ghanatbari, M., Mehridehnavi, A., Rabbani, H., et al: ‘A comparative study of the output correlations between wavelet transform, neural and neuro fuzzy networks and BIS index for depth of anesthesia’, in Seroji, M.N. (Eds.): ‘A comparative study of the output correlations between wavelet transform, neural and neuro fuzzy networks and BIS index for depth of anesthesia’ (IEEE, 2010), pp. 655–659.
9. 9)
  - 16. Kortelainen, J., Vayrynen, E., Jia, X., et al: ‘EEG-based detection of awakening from isoflurane anesthesia in rats’, in Lovell, N. (Eds.): ‘EEG-based detection of awakening from isoflurane anesthesia in rats’ (IEEE, 2012), pp. 4279–4282.
10. 10)
  - 20. Jordan, D., Stockmanns, G., Kochs, E.F., et al: ‘Electroencephalographic order pattern analysis for the separation of consciousness and unconsciousness: an analysis of approximate entropy, permutation entropy, recurrence rate, and phase coupling of order recurrence plots’, Anesthesiology, 2008, 109, (6), pp. 1014–1022.
11. 11)
  - 3. Wei, Q., Liu, Q., Fan, S.Z., et al: ‘Analysis of EEG via multivariate empirical mode decomposition for depth of anesthesia based on sample entropy’, Entropy , 2013, 15, (9), pp. 3458–3470.
12. 12)
  - 18. Pachori, R.B., Patidar, S.: ‘Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions’, Comput. Methods Programs Biomed., 2014, 113, (2), pp. 494–502.
13. 13)
  - 6. Kortelainen, J., Vayrynen, E., Seppanen, T.: ‘Depth of anesthesia during multidrug infusion: separating the effects of propofol and remifentanil using the spectral features of EEG’, IEEE Trans. Biomed. Eng., 2011, 58, (5), pp. 1216–1223.
14. 14)
  - 26. Liang, Z., Li, D., Li, X.: ‘Comparison of permutation entropy index and bispectral index for monitoring effects of anesthetic drugs to brain activity’, in Shi, R., Fu, W., Wang, Y., Wang, H. (Eds.): ‘Comparison of permutation entropy index and bispectral index for monitoring effects of anesthetic drugs to brain activity’ (IEEE, 2009), pp. 1–5.
15. 15)
  - 23. Donoho, D.L.: ‘De-noising by soft-thresholding’, IEEE Trans. Inf. Theory, 1995, 41, (3), pp. 613–627.
16. 16)
  - 14. Rezek, I., Roberts, S.J., Conradt, R.: ‘Increasing the depth of anesthesia assessment’, IEEE Eng. Med. Biol. Mag., 2007, 26, (2), pp. 64–73.
17. 17)
  - 4. Liu, Q., Wei, Q., Fan, S.Z., et al: ‘Adaptive computation of multiscale entropy and its application in EEG signals for monitoring depth of anesthesia during surgery’, Entropy , 2012, 14, (6), pp. 978–992.
18. 18)
  - 13. Liang, Z., Li, D., Ouyang, G., et al: ‘Multiscale rescaled range analysis of EEG recordings in sevoflurane anesthesia’, Clin. Neurophysiol., 2012, 123, (4), pp. 681–688.
19. 19)
  - 24. Li, T., Wen, P., Jayamaha, S.: ‘Anaesthetic EEG signal denoise using improved nonlocal mean methods’, Australas. Phys. Eng. Sci. Med., 2014, 37, (2), pp. 431–437.
20. 20)
  - 22. Rodríguez, J.J., Alonso, C.J., Maestro, J.A.: ‘Support vector machines of interval-based features for time series classification’, Knowl.-Based Syst., 2005, 18, (4), pp. 171–178.
21. 21)
  - 15. Wong, K.F.K., Smith, A.C., Pierce, E.T., et al: ‘Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia’, in Lovell, N. (Eds.): ‘Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia’ (IEEE, 2011), pp. 4705–4708.
22. 22)
  - 9. Castro, A., Almeida, F.G., Amorim, P., et al: ‘A wavelet based method for steady-state detection in anesthesia’, in Liang, Z.-P. (Eds.): ‘A wavelet based method for steady-state detection in anesthesia’ (IEEE, 2009), pp. 954–957.
23. 23)
  - 5. Nguyen-Ky, T., Wen, P., Li, Y.: ‘An improved detrended moving-average method for monitoring the depth of anesthesia’, IEEE Trans. Biomed. Eng., 2010, 57, (10), pp. 2369–2378.
24. 24)
  - 11. Nguyen-Ky, T., Wen, P., Li, Y.: ‘Consciousness and depth of anesthesia assessment based on Bayesian analysis of EEG signals’, IEEE Trans. Biomed. Eng., 2013, 60, (6), pp. 1488–1498.
25. 25)
  - 2. Fan, S.Z., Yeh, J.R., Chen, B.C., et al: ‘Comparison of EEG approximate entropy and complexity measures of depth of anaesthesia during inhalational general anaesthesia’, J. Med. Biol. Eng., 2011, 31, (5), pp. 359–366.
26. 26)
  - 25. Kuncheva, L.I., Rodríguez, J.J.: ‘Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis’, Prog. Artif. Intell., 2013, 2, (1), pp. 1–8.
27. 27)
  - 1. Li, X., Sleigh, J., Voss, L., et al: ‘Interpretation of anesthetic drug effect using recurrent dynamics of EEG recordings’, Neurosci. Lett., 2007, 424, (1), pp. 47–50.

Depth of anaesthesia assessment using interval second-order difference plot and permutation entropy techniques

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