Comparison of entropy and complexity measures for the assessment of depth of sedation

Entropy and complexity of the electroencephalogram (EEG) have recently been proposed as measures of depth of anesthesia and sedation. Using surrogate data of predefined spectrum and probability distribution we show that the various algorithms used for the calculation of entropy and complexity actually measure different properties of the signal. The tested methods, Shannon entropy (ShEn), spectral entropy, approximate entropy (ApEn), Lempel-Ziv complexity (LZC), and Higuchi fractal dimension (HFD) are then applied to the EEG signal recorded during sedation in the intensive care unit (ICU). It is shown that the applied measures behave in a different manner when compared to clinical depth of sedation score the Ramsay score. ShEn tends to increase while the other tested measures decrease with deepening sedation. ApEn, LZC, and HFD are highly sensitive to the presence of high-frequency components in the EEG signal.

[1]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[2]  T. Schreiber,et al.  Surrogate time series , 1999, chao-dyn/9909037.

[3]  F. A. Seiler,et al.  Numerical Recipes in C: The Art of Scientific Computing , 1989 .

[4]  T. Lipping,et al.  Higuchi fractal dimension and spectral entropy as measures of depth of sedation in intensive care unit , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  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.

[6]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[7]  T. Inouye,et al.  Quantification of EEG irregularity by use of the entropy of the power spectrum. , 1991, Electroencephalography and clinical neurophysiology.

[8]  D. Levy,et al.  Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. , 1997, Circulation.

[9]  P. Agostino Accardo,et al.  Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.

[10]  I. Kissin,et al.  General anesthetic action: an obsolete notion? , 1993, Anesthesia and Analgesia.

[11]  William H. Press,et al.  Book-Review - Numerical Recipes in Pascal - the Art of Scientific Computing , 1989 .

[12]  A. Yli-Hankala,et al.  Time‐frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia , 2004, Acta anaesthesiologica Scandinavica.

[13]  I. Rezek,et al.  Stochastic complexity measures for physiological signal analysis , 1998, IEEE Transactions on Biomedical Engineering.

[14]  Erik W. Jensen,et al.  EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..

[15]  I. Rampil A Primer for EEG Signal Processing in Anesthesia , 1998, Anesthesiology.

[16]  Abraham Lempel,et al.  On the Complexity of Finite Sequences , 1976, IEEE Trans. Inf. Theory.

[17]  M. Tribus,et al.  Energy and information , 1971 .

[18]  T. Higuchi Approach to an irregular time series on the basis of the fractal theory , 1988 .

[19]  Erkki Oja,et al.  Independent Component Analysis , 2001 .

[20]  J. Bruhn,et al.  Approximate Entropy as an Electroencephalographic Measure of Anesthetic Drug Effect during Desflurane Anesthesia , 2000, Anesthesiology.

[21]  J. Sleigh,et al.  Does bispectral analysis of the electroencephalogram add anything but complexity? , 2004, British journal of anaesthesia.

[22]  M. Ramsay,et al.  Controlled Sedation with Alphaxalone-Alphadolone , 1974, British medical journal.

[23]  Schreiber,et al.  Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.

[24]  Maria V. Sanchez-Vives,et al.  Application of Lempel–Ziv complexity to the analysis of neural discharges , 2003, Network.

[25]  M. van Gils,et al.  Application of spectral entropy to EEG and facial EMG frequency bands for the assessment of level of sedation in ICU , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[26]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[27]  J. Bruhn,et al.  Shannon Entropy Applied to the Measurement of the Electroencephalographic Effects of Desflurane , 2001, Anesthesiology.

[28]  A. Yli-Hankala,et al.  Description of the Entropy™ algorithm as applied in the Datex‐Ohmeda S/5™ Entropy Module , 2004, Acta anaesthesiologica Scandinavica.