Presenting a Combinatorial Feature to Estimate Depth of Anesthesia
Determining depth of anesthesia is a challenging problem
in the context of biomedical signal processing. Various methods
have been suggested to determine a quantitative index as depth of
anesthesia, but most of these methods suffer from high sensitivity
during the surgery. A novel method based on energy scattering of
samples in the wavelet domain is suggested to represent the basic
content of electroencephalogram (EEG) signal. In this method, first
EEG signal is decomposed into different sub-bands, then samples
are squared and energy of samples sequence is constructed through
each scale and time, which is normalized and finally entropy of the
resulted sequences is suggested as a reliable index. Empirical Results
showed that applying the proposed method to the EEG signals can
classify the awake, moderate and deep anesthesia states similar to
BIS.
[1] B. A. Orser, Depth of anesthesia monitor and the frequency of intraoperative
awareness, The New England Journal of Medicine, vol. 358, 2008,
pp. 1189-1191.
[2] H.L. Kaul, N. Bharti, Monitoring depth of anesthesia, Indian J. Anesth.,
vol. 46, 2002, pp. 323-332.
[3] L. Shao-hua, W. Wei, D. Guan-nan, K. Jing-dong, H. Fang-xiao, T. Ming,
Relationship between depth of anesthesia and effect-site concentration of
propofol during induction with the target-controlled infusion technique in
elderly patients, Chinese Medical Journal, vol. 122, 2009, pp. 935-940.
[4] P. S. Sebel, T. A. Bowdle, M. M. Ghoneim, I. J. Rampil, R. E. Padilla, T.
J. Gan, and K. B. Domino, The incidence of awareness during anesthesia:
A multicenter United States study, Anesth. Analgesia, vol. 99, 2004, pp.
833-839.
[5] D. R. Stanski, Monitoring depth of anesthesia, in Anesthesia, R. D. Miller,
Ed. Anesthesia, (Churchill Livingstone, New York, 1994, pp. 1127-1159).
[6] E. W. Jensen, P. Lindholm, and S. Henneberg, Autoregressive modeling
with exogenous input of middle-latency auditory-evoked potentials to
measure rapid changes in depth of anaesthesia, Meth. Inf. Med. vol. 35,
1996, pp. 256-260.
[7] H. Litvan, E. W. Jensen, J. Galan, J. Lund, B. E. Rodriguez, S. W.
Henneberg, P. Caminal, and J. M. Villar Landeira, Comparison of
conventional averaged and rapid averaged, autoregressive-based extracted
auditory evoked potentials for monitoring the hypnotic level during
propofol induction, Anesthesiology, vol. 97, 2002, pp. 351-358.
[8] J. Rampil, A primer for EEG signal processing in anesthesia, Anesthesiology,
vol. 89, 1998, pp. 981-1001.
[9] J. C. Sigl and N. G. Chamoun, An introduction to Bispectral analysis
for the EEG, Journal of Clinical Monitoring and Computing Springer
Netherlands, vol. 10, 1994, pp. 392-404.
[10] P.Ch. Ivanov, A.N. Amaral, Lus, A.L. Goldberger, S. Havlin, M.G.
Rosenblum, Z.R. Struzik, H.E. Stanley, Multifractality in human heartbeat
dynamics, Nature. vol. 399, 1999, pp. 461-465.
[11] D. Garrett, D. A. Peterson, C. W. Anderson, M. H. Thaut, Comparison
of Linear, Nonlinear, and Feature Selection Methods for EEG Signal
Classification, IEEE Trans. neural systems and rehabilitation engineering,
vol. 11, 2003, pp. 141-144.
[12] P. Flandrin, Time-Frequency or Time-Scale Analysis, Academic Press,
London, 1999.
[13] F. Hlawatsch, G.F. Boudreaux-Bartels, Linear and quadratic timefrequency
signal representations, IEEE Signal Process. Mag., vol. 9, 1992,
pp. 21-67.
[14] T. W. Schnider, C. F. Minto, S. L. Shafer, P. L. Gambus, C. Andresen,
D. B. Goodale, and E. J. Youngs, The influence of age on propofol
pharmacodynamics, Anesthesiology, vol. 90, 1999, pp. 1502-1516.
[15] S. L. Shafer and K. M. Gregg, Algorithms to rapidly achieve and
maintain stable drug concentrations at the site of drug effect with a
computer- controlled infusion pump, Journal of Pharmacokinetics and
Pharmacodynamics, Springer, vol. 20, 1992, pp. 147-169.
[16] M. M. R. F. Struys, T. De Smet, B. Depoorter, L. F. Versichelen, E.
P. Mortier, F. J. Dumortier, S. L. Shafer, and G. Rolly, Comparison
of plasma compartment versus two methods for effect compartmentcontrolled
target-controlled infusion for propofol, Anesthesiology, vol.
92, 2000, pp. 399-406.
[17] Vigon L, Saatchi M R, Mayhew J E W and Fernandes R, Quantitative
evaluation of techniques for ocular artifact filtering of EEG waveforms,
IEE Proceedings on Science Measurement Technology, vol. 147, n.5, Sep
2000.
[18] Girton D G, Kamiya J, A simple on-line technique for removing eye
movement artifacts from the EEG, Electroencephalography and Clinical
Neurophysiology, vol. 34, pp. 212-216, 1973.
[19] V. J. Samar, A. Bopardikar, R. Rao, Kenneth Swartz, Wavelet Analysis
of Neuroelectric waveforms: A Conceptual Tutorial, Brain and Laguage,
vol. 66, 1999, pp. 7-60.
[20] T Gasser, L Sroka and J Mocks, The transfer of EOG activity into
the EEG for eyes open and closed, Electroencephalography and clinical
neurophysiology, vol. 61, 1985, pp. 181-193.
[21] http://www.xploretat.de/tutorials/waveframe8.html
[22] V. krishnaveni, S. jayaraman, N. malmurugan, A. kandaswamy, K. ramadoss,
Non adaptive thresholding methods for correcting ocular artifacts
in EEG, academic open internet journal, vol. 13, 2004.
[23] M. Nakamura, H. Shibasaki, Elimination of EKG artifacts from EEG
records: a new method of noncephalic referential EEG recording Electroencephalogr,
Clin. Neurophys. vol. 66, 1987, pp. 89-92.
[24] H.J. Park, D.U. Jeong, K.S. Park, Automated detection and elimination
of periodic ECG artifacts in EEG using the energy interval histogram
method, IEEE Trans. Biomed. Eng. vol. 49 n.12, 2002, pp.1526-1533.
[25] N.V. Thakor, J.G. Webster, W.J. Tompkins, Estimation of QRS complex
power spectra for design of a QRS filter, IEEE Trans. Biomed. Eng. vol.
31, 1984, pp. 702-705.
[26] J. A. Jiang, C. F. Chao, M. J. Chiu, R. G. Lee, C. L. Tseng, R. Lin, An
automatic analysis method for detecting and eliminating ECG artifacts
in EEG, Computers in Biology and Medicine, vol. 37, 2007, pp. 1660 -
1671.
[27] H. A. Al-Nashash, J. S. Paul, N. V. Thakor, Wavelet entropy Method
for EEG Analysis: Application to Global Brain Injury, 1st International
IEEE EMBS Conf. on Neural Engineering, Capri Island, Italy, 2003, pp.
348-351.
[28] M. Mikaili, S. Hashemi, Assesment of the complexity/regularity of
transient brain waves (EEG) during sleep, based on wavelet theory and
the concept of of entropy, Iranian J. of science and Technology, vol. 26,
pp.639-646, 2002.
[29] O. A. Rosso, S. Blanco, A. Rabinowicz, "Wavelet analysis of generalized
tonic-clonic epileptic seizures," Signal Processing, vol. 83 n.6, June 2003,
pp. 1275-1289.
[30] R. Hornero, D. E. Abasolo, P. Espino, "Use of wavelet entropy to
compare the EEG background activity of epileptic patients and control
patients," in Proc. 7th International Symposium, vol. 2, 2003, pp. 5-8.
[31] T. Zikov, S. Bibian, G. A. Dumont, M. Huzmezan,C. R. Ries, Quantifying
Cortical Activity During General Anesthesia Using Wavelet Analysis,
IEEE Trans. On biomedical engineering, vol. 53, April 2006.
[1] B. A. Orser, Depth of anesthesia monitor and the frequency of intraoperative
awareness, The New England Journal of Medicine, vol. 358, 2008,
pp. 1189-1191.
[2] H.L. Kaul, N. Bharti, Monitoring depth of anesthesia, Indian J. Anesth.,
vol. 46, 2002, pp. 323-332.
[3] L. Shao-hua, W. Wei, D. Guan-nan, K. Jing-dong, H. Fang-xiao, T. Ming,
Relationship between depth of anesthesia and effect-site concentration of
propofol during induction with the target-controlled infusion technique in
elderly patients, Chinese Medical Journal, vol. 122, 2009, pp. 935-940.
[4] P. S. Sebel, T. A. Bowdle, M. M. Ghoneim, I. J. Rampil, R. E. Padilla, T.
J. Gan, and K. B. Domino, The incidence of awareness during anesthesia:
A multicenter United States study, Anesth. Analgesia, vol. 99, 2004, pp.
833-839.
[5] D. R. Stanski, Monitoring depth of anesthesia, in Anesthesia, R. D. Miller,
Ed. Anesthesia, (Churchill Livingstone, New York, 1994, pp. 1127-1159).
[6] E. W. Jensen, P. Lindholm, and S. Henneberg, Autoregressive modeling
with exogenous input of middle-latency auditory-evoked potentials to
measure rapid changes in depth of anaesthesia, Meth. Inf. Med. vol. 35,
1996, pp. 256-260.
[7] H. Litvan, E. W. Jensen, J. Galan, J. Lund, B. E. Rodriguez, S. W.
Henneberg, P. Caminal, and J. M. Villar Landeira, Comparison of
conventional averaged and rapid averaged, autoregressive-based extracted
auditory evoked potentials for monitoring the hypnotic level during
propofol induction, Anesthesiology, vol. 97, 2002, pp. 351-358.
[8] J. Rampil, A primer for EEG signal processing in anesthesia, Anesthesiology,
vol. 89, 1998, pp. 981-1001.
[9] J. C. Sigl and N. G. Chamoun, An introduction to Bispectral analysis
for the EEG, Journal of Clinical Monitoring and Computing Springer
Netherlands, vol. 10, 1994, pp. 392-404.
[10] P.Ch. Ivanov, A.N. Amaral, Lus, A.L. Goldberger, S. Havlin, M.G.
Rosenblum, Z.R. Struzik, H.E. Stanley, Multifractality in human heartbeat
dynamics, Nature. vol. 399, 1999, pp. 461-465.
[11] D. Garrett, D. A. Peterson, C. W. Anderson, M. H. Thaut, Comparison
of Linear, Nonlinear, and Feature Selection Methods for EEG Signal
Classification, IEEE Trans. neural systems and rehabilitation engineering,
vol. 11, 2003, pp. 141-144.
[12] P. Flandrin, Time-Frequency or Time-Scale Analysis, Academic Press,
London, 1999.
[13] F. Hlawatsch, G.F. Boudreaux-Bartels, Linear and quadratic timefrequency
signal representations, IEEE Signal Process. Mag., vol. 9, 1992,
pp. 21-67.
[14] T. W. Schnider, C. F. Minto, S. L. Shafer, P. L. Gambus, C. Andresen,
D. B. Goodale, and E. J. Youngs, The influence of age on propofol
pharmacodynamics, Anesthesiology, vol. 90, 1999, pp. 1502-1516.
[15] S. L. Shafer and K. M. Gregg, Algorithms to rapidly achieve and
maintain stable drug concentrations at the site of drug effect with a
computer- controlled infusion pump, Journal of Pharmacokinetics and
Pharmacodynamics, Springer, vol. 20, 1992, pp. 147-169.
[16] M. M. R. F. Struys, T. De Smet, B. Depoorter, L. F. Versichelen, E.
P. Mortier, F. J. Dumortier, S. L. Shafer, and G. Rolly, Comparison
of plasma compartment versus two methods for effect compartmentcontrolled
target-controlled infusion for propofol, Anesthesiology, vol.
92, 2000, pp. 399-406.
[17] Vigon L, Saatchi M R, Mayhew J E W and Fernandes R, Quantitative
evaluation of techniques for ocular artifact filtering of EEG waveforms,
IEE Proceedings on Science Measurement Technology, vol. 147, n.5, Sep
2000.
[18] Girton D G, Kamiya J, A simple on-line technique for removing eye
movement artifacts from the EEG, Electroencephalography and Clinical
Neurophysiology, vol. 34, pp. 212-216, 1973.
[19] V. J. Samar, A. Bopardikar, R. Rao, Kenneth Swartz, Wavelet Analysis
of Neuroelectric waveforms: A Conceptual Tutorial, Brain and Laguage,
vol. 66, 1999, pp. 7-60.
[20] T Gasser, L Sroka and J Mocks, The transfer of EOG activity into
the EEG for eyes open and closed, Electroencephalography and clinical
neurophysiology, vol. 61, 1985, pp. 181-193.
[21] http://www.xploretat.de/tutorials/waveframe8.html
[22] V. krishnaveni, S. jayaraman, N. malmurugan, A. kandaswamy, K. ramadoss,
Non adaptive thresholding methods for correcting ocular artifacts
in EEG, academic open internet journal, vol. 13, 2004.
[23] M. Nakamura, H. Shibasaki, Elimination of EKG artifacts from EEG
records: a new method of noncephalic referential EEG recording Electroencephalogr,
Clin. Neurophys. vol. 66, 1987, pp. 89-92.
[24] H.J. Park, D.U. Jeong, K.S. Park, Automated detection and elimination
of periodic ECG artifacts in EEG using the energy interval histogram
method, IEEE Trans. Biomed. Eng. vol. 49 n.12, 2002, pp.1526-1533.
[25] N.V. Thakor, J.G. Webster, W.J. Tompkins, Estimation of QRS complex
power spectra for design of a QRS filter, IEEE Trans. Biomed. Eng. vol.
31, 1984, pp. 702-705.
[26] J. A. Jiang, C. F. Chao, M. J. Chiu, R. G. Lee, C. L. Tseng, R. Lin, An
automatic analysis method for detecting and eliminating ECG artifacts
in EEG, Computers in Biology and Medicine, vol. 37, 2007, pp. 1660 -
1671.
[27] H. A. Al-Nashash, J. S. Paul, N. V. Thakor, Wavelet entropy Method
for EEG Analysis: Application to Global Brain Injury, 1st International
IEEE EMBS Conf. on Neural Engineering, Capri Island, Italy, 2003, pp.
348-351.
[28] M. Mikaili, S. Hashemi, Assesment of the complexity/regularity of
transient brain waves (EEG) during sleep, based on wavelet theory and
the concept of of entropy, Iranian J. of science and Technology, vol. 26,
pp.639-646, 2002.
[29] O. A. Rosso, S. Blanco, A. Rabinowicz, "Wavelet analysis of generalized
tonic-clonic epileptic seizures," Signal Processing, vol. 83 n.6, June 2003,
pp. 1275-1289.
[30] R. Hornero, D. E. Abasolo, P. Espino, "Use of wavelet entropy to
compare the EEG background activity of epileptic patients and control
patients," in Proc. 7th International Symposium, vol. 2, 2003, pp. 5-8.
[31] T. Zikov, S. Bibian, G. A. Dumont, M. Huzmezan,C. R. Ries, Quantifying
Cortical Activity During General Anesthesia Using Wavelet Analysis,
IEEE Trans. On biomedical engineering, vol. 53, April 2006.
@article{"International Journal of Medical, Medicine and Health Sciences:63947", author = "Toktam Zoughi and Reza Boostani", title = "Presenting a Combinatorial Feature to Estimate Depth of Anesthesia", abstract = "Determining depth of anesthesia is a challenging problem
in the context of biomedical signal processing. Various methods
have been suggested to determine a quantitative index as depth of
anesthesia, but most of these methods suffer from high sensitivity
during the surgery. A novel method based on energy scattering of
samples in the wavelet domain is suggested to represent the basic
content of electroencephalogram (EEG) signal. In this method, first
EEG signal is decomposed into different sub-bands, then samples
are squared and energy of samples sequence is constructed through
each scale and time, which is normalized and finally entropy of the
resulted sequences is suggested as a reliable index. Empirical Results
showed that applying the proposed method to the EEG signals can
classify the awake, moderate and deep anesthesia states similar to
BIS.", keywords = "Depth of anesthesia, EEG, BIS, Wavelet transforms.", volume = "4", number = "1", pages = "46-5", }
