Patients' consciousness analysis using dynamic approximate entropy and MEMD method

Electroencephalography (EEG) based preliminary examination has been proposed in the clinical brain death determination. Multivariate empirical mode decomposition(MEMD) and approximate entropy(ApEn) are often used in the EEG signal analysis process. MEMD is an extended approach of empirical mode decomposition(EMD), in which it overcomes the problem of the decomposed number and frequency, and enables to extract brain activity features from multi-channel EEG simultaneously. ApEn as a complexity based method appears to have potential for the application to physiological and clinical time series data. In our previous studies, MEMD method and ApEn measure were always used severally, if MEMD and ApEn are used to analysis the same EEG signal simultaneously, the result of experiment will be more accurate. In this paper, we present MEMD method and ApEn measure based blind test without knowing about the clinical symptoms of patients beforehand. Features obtained from two typical cases indicate one patient being in coma and another in quasi-brain-death state.

[1]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[2]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Zhe Chen,et al.  An Empirical Quantitative EEG Analysis for Evaluating Clinical Brain Death , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[5]  Zhe Chen,et al.  Advanced EEG Signal Processing in Brain Death Diagnosis , 2008 .

[6]  Toshihisa Tanaka,et al.  Complex Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[7]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[8]  Gabriel Rilling,et al.  Bivariate Empirical Mode Decomposition , 2007, IEEE Signal Processing Letters.

[9]  D. P. Mandic,et al.  Multivariate empirical mode decomposition , 2010, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Muhammad Altaf,et al.  Rotation Invariant Complex Empirical Mode Decomposition , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[11]  Andrzej Cichocki,et al.  A robust approach to independent component analysis of signals with high-level noise measurements , 2003, IEEE Trans. Neural Networks.

[12]  Danilo P. Mandic,et al.  Empirical Mode Decomposition for Trivariate Signals , 2010, IEEE Transactions on Signal Processing.