Abstract
In this paper, subband wavelet entropy (SWE) is used for the segmentation of electroencephalographic signals (EEG) recorded during injury and recovery following global cerebral ischemia. Wavelet analysis is used to decompose the EEG into standard clinical subbands followed by computation of the Shannon entropy. The EEG was measured from rodent brains in a controlled experimental brain injury model by hypoxic-ischemic cardiac arrest. Results show that while the relative EEG power failed to reveal the order of bursting activity associated with recovery, SWE was used to segment the EEG and delineate the initial bursting periods in each subband. Based on entropy variations obtained from a cohort of animals with graded levels of hypoxic-ischemic cardiac arrest, an intermittent pattern of bursting was observed in the high frequency bands. © 2003 Biomedical Engineering Society.
PAC2003: 8719Nn, 8719Hh, 8719La, 8710+e
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Agarwal, R., and J. Gotman. Adaptive segmentation of electroencephalographic data using a nonlinear energy operator. Presented at the IEEE International Symposium on Circuits and Systems, ISCAS' 99, 1999.
Agarwal, R., J. Gotman, D. Flanagan, and B. Rosenblatt. Automatic EEG analysis during long-term monitoring in the ICU. Electroencephalogr. Clin. Neurophysiol.107:44–58, 1998.
Bercher, J., and C. Vignat. Estimating the entropy of a signal with Applications. IEEE Signal Process. Mag.48:1687–1694, 2000.
Bezerianos, A., S. Tong, and N. Thakor, Information measures of brain dynamics. Presented at the Nonlinear Signal and Image Processing Workshop, Baltimore, MD, 2001.
Borel, C., and D. Hanley. Neurologic intensive care unit monitoring. Crit. Care Clin.1:223–239, 1985.
Chui, C. An Introduction to Wavelets'. New York: Academic, 1991.
Flandrin, P., R. Baraniuk, and O. Michael. Time-frequency complexity and Information. Presented at the IEEE International Conference on Acoustics, Speech, and Signal Proceeding, ICASSP' 94, 1994.
Geocadin, R. G., R. Ghodadra, T. Kimura, H. Lei, D. L. Sherman, D. F. Hanley, and N. V. Thakor. Novel quantitative EEG injury measure of global cerebral ischemia. Clin. Neurophysiol.111:1779–1787, 2000.
Goel, V., A. M. Brambrink, A. Baykal, R. C. Koehler, D. F. Hanley, and N. V. Thakor. Dominant frequency analysis of EEG reveals brain's response during injury and recovery. IEEE Trans. Biomed. Eng.43:1083–1092, 1996.
Gray, R. Entropy and Information Theory. New York: Springer, 1990.
Jonkman, E., A. Van Huffelen, and G. Pfurtscheller. Quantitative EEG in cerebral ischemia (Clinical applications of computer analysis of EEG and other neurophysiological signals). In: Handbook of Electroencephalography and Clinical Neurophysiology, edited by L. d. S. FH, S. v. L. W, and R. A. Amsterdam: Elsevier, 1986.
Katz, L., U. Ebmeyer, P. Safar, A. Radovsky, and R. Neumar. Outcome model of asphyxial cardiac arrest in rats. J. Cereb. Blood Flow Metab.15:1032–1039, 1995.
Kraaier, V., A. C. van Huffelen, and G. H. Wieneke. Changes in quantitative EEG and blood flow velocity due to standardized hyperventilation: A model of transient ischaemia in young human subjects. Electroencephalogr. Clin. Neurophysiol.70:377–387, 1988.
Niedermeyer, E., D. L. Sherman, R. J. Geocadin, H. C. Hansen, and D. F. Hanley. The burst–suppression electroencephalogram. Clin. Electroencephalogr.30:99–105, 1999.
Pfurtscheller, G. Special uses of EEG computer analysis in clinical environments. In: Electroencephalography. Basic Principles, Clinical Applications, and Related Fields, edited by N. E and F. H. Lopes da Silva. Baltimore (MD): Williams and Wilkins, 1998, pp. 1215–1223.
Pfurtscheller, G., A. Stancak, Jr., and G. Edlinger. On the existence of different types of central beta rhythms below 30 Hz. Electroencephalogr. Clin. Neurophysiol.102:316–325, 1997.
Rosso, O. A., S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schurmann, and E. Basar. Wavelet Entropy: A new tool for analysis of short duration brain electrical signals. J. Neurosci. Methods105:65–75, 2001.
Schwibbe, M., A. Breull, and D. Becker. Peak centered power spectra: A successful attempt to calculate efficient parameters in the alpha range of EEG. Electroencephalogr. Clin. Neurophysiol.52:497–500, 1981.
Shah, S., A. El-Jaroudi, P. Loughlin, L. Chaparro, P. Flandrin, and W. Martin. Signal synthesis and positive time-frequency distributions. J. Franklin Inst.337:317–328, 2000.
Sherman, D. L., A. M. Brambrink, R. N. Ichord, V. K. Dasika, R. C. Koehler, R. J. Traystman, D. F. Hanley, and N. V. Thakor. Quantitative EEG during early recovery from hypoxic-ischemic injury in immature piglets: Burst occurrence and duration,” Clin. Electroencephalogr30:175–183, 1999.
Thakor, N., and D. L. Sherman. Wavelet (time-scale) analysis in biomedical signal processing. CRC Crit. Rev. Biomed. Eng.58:886–906, 1995.
Van der Worp, H. B., V. Kraaier, G. H. Wieneke, and A. C. Van Huffelen. Quantitative EEG during progressive hypocarbia and hypoxia. Hyperventilation-induced EEG changes reconsidered. Electroencephalogr. Clin. Neurophysiol.79:335–341, 1991.
Van Huffelen, A., D. Poortvliet, C. Van der Wulp, and O. Magnus. Quantitative EEG in cerebral ischemia. In: EEG and Clinical Neurophysiology, edited by L. H. and A. A. Amsterdam: Excerpta Medica, 1980, pp. 125–137.
Williams, W., M. Brown, and A. Hero. Uncertainty, information and time–frequency distributions. Presented at SPIE, 1991.
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Al-Nashash, H.A., Paul, J.S., Ziai, W.C. et al. Wavelet Entropy for Subband Segmentation of EEG During Injury and Recovery. Annals of Biomedical Engineering 31, 653–658 (2003). https://doi.org/10.1114/1.1575757
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DOI: https://doi.org/10.1114/1.1575757