Commenced in January 2007
Paper Count: 30184
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083923Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1415
 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.
 H.L. Kaul, N. Bharti, Monitoring depth of anesthesia, Indian J. Anesth., vol. 46, 2002, pp. 323-332.
 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.
 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.
 D. R. Stanski, Monitoring depth of anesthesia, in Anesthesia, R. D. Miller, Ed. Anesthesia, (Churchill Livingstone, New York, 1994, pp. 1127-1159).
 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.
 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.
 J. Rampil, A primer for EEG signal processing in anesthesia, Anesthesiology, vol. 89, 1998, pp. 981-1001.
 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.
 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.
 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.
 P. Flandrin, Time-Frequency or Time-Scale Analysis, Academic Press, London, 1999.
 F. Hlawatsch, G.F. Boudreaux-Bartels, Linear and quadratic timefrequency signal representations, IEEE Signal Process. Mag., vol. 9, 1992, pp. 21-67.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.
 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.