Abstract
The recently introduced multivariate multiscale sample entropy (MMSE) well evaluates the long correlations in multiple channels, so that it can reveal the complexity of multivariate biological signals. The existing MMSE algorithm deals with short time series statically whereas long time series are common for real-time computation in practical use. As a solution, we novelly proposed our time-dependent MMSE as an extension of MMSE. This helps us gain greater insight into the complexity of each section of time series, respectively, producing multifaceted and more robust estimates than the standard MMSE. The simulation results illustrated the effectiveness and well performance in the brain death diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cao, J., Chen, Z.: Advanced EEG signal processing in brain death diagnosis. In: Signal Processing Techniques for Knowledge Extraction and Information Fusion, pp. 275–297. Springer (2008)
Guatama, T., Mandic, D.P., Van Hulle, M.M.: The delay vector variance method for detecting determinism and nonlinearity in time series. Physica D: Nonlinear Phenomena 190(3-4), 167–176 (2004)
Eelco, F., Wijdicks, M.: Brain death worldwide. Neurology 58, 20–25 (2002)
Taylor, R.M.: Reexaming the definition and criteria of death. Seminars in Neurology 17, 265–270 (1997)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences of the United State of America 88(6), 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. AJP-Heart and Circulatory Physiology 278(6), 2039–2049 (2000)
Costa, M., Goldberger, A.L., Peng, C.-K.: Multiscale entropy analysis of complex physiologic time series. Physical Review Letters 89(6), 68–102 (2002)
Hu, M., Liang, H.: Adaptive multiscale entropy analysis of multivariate neural data. IEEE Transactions on Biomedical Engineering 59(1), 12–15 (2011)
Ahmed, M.U., Li, L., Cao, J., Mandic, D.P.: Multivariate multiscale entropy for brain consciousness analysis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, pp. 810–813 (2011)
Cao, L., Mees, A., Judd, K.: Dynamic from multivariate time series. Physica D: Nonlinear Phenomena 121(1-2), 75–88 (1998)
Li, L., Saito, Y., Looney, D., Cao, J., Tanaka, T., Mandic, D.P.: Data fusion via fission for the analysis of brain death. In: Evolving Intelligent Systems: Methodology and Applications, pp. 279–320. Springer (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ni, L., Cao, J., Wang, R. (2013). Time-Dependent Multivariate Multiscale Entropy Based Analysis on Brain Consciousness Diagnosis. In: Liu, D., Alippi, C., Zhao, D., Hussain, A. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2013. Lecture Notes in Computer Science(), vol 7888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38786-9_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-38786-9_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38785-2
Online ISBN: 978-3-642-38786-9
eBook Packages: Computer ScienceComputer Science (R0)