Detection of burst suppression patterns in EEG using recurrence rate

Zhenhu Liang, Yinghua Wang, Yongshao Ren, Duan Li, Logan Voss, Jamie Sleigh, Xiaoli Li, Zhenhu Liang, Yinghua Wang, Yongshao Ren, Duan Li, Logan Voss, Jamie Sleigh, Xiaoli Li

Abstract

Burst suppression is a unique electroencephalogram (EEG) pattern commonly seen in cases of severely reduced brain activity such as overdose of general anesthesia. It is important to detect burst suppression reliably during the administration of anesthetic or sedative agents, especially for cerebral-protective treatments in various neurosurgical diseases. This study investigates recurrent plot (RP) analysis for the detection of the burst suppression pattern (BSP) in EEG. The RP analysis is applied to EEG data containing BSPs collected from 14 patients. Firstly we obtain the best selection of parameters for RP analysis. Then, the recurrence rate (RR), determinism (DET), and entropy (ENTR) are calculated. Then RR was selected as the best BSP index one-way analysis of variance (ANOVA) and multiple comparison tests. Finally, the performance of RR analysis is compared with spectral analysis, bispectral analysis, approximate entropy, and the nonlinear energy operator (NLEO). ANOVA and multiple comparison tests showed that the RR could detect BSP and that it was superior to other measures with the highest sensitivity of suppression detection (96.49%, P = 0.03). Tracking BSP patterns is essential for clinical monitoring in critically ill and anesthetized patients. The purposed RR may provide an effective burst suppression detector for developing new patient monitoring systems.

Trial registration: ClinicalTrials.gov NCT00446212.

Figures

Figure 1
Figure 1
The block diagrams of EEG signal processing.
Figure 2
Figure 2
(a) A composite EEG signal from a patient. It consists of suppression (1000-point), burst (1000-point), and normal (1000-point) records artificially joined together; (b) different RP patterns during suppression, burst, and normal states, respectively. The blue box represents the suppression, the green box the burst, and the red box the normal state.
Figure 3
Figure 3
The embedding dimension and delay time of the EEG signals during the burst suppression state. (a) The false nearest neighbors versus the dimension with scales from 0 to 40. (b) The local plot of (a) with the dimension scales from 1 to 10. (c) The mutual information versus the delay time with scales from 0 to 40. (d) The local plot of (c) with the delay time scales from 1 to 10.
Figure 4
Figure 4
(a) An EEG signal consists of suppression and burst; (b) the different RP under four different radiuses for the signals in (a). (A) r = 0.1, (B) r = 0.3, (C) r = 0.5, and (D) r = 0.7 with a dimension of 4 and a delay of 3.
Figure 5
Figure 5
The boxplot of three different indexes at the burst suppression normal states. (a) The RR index, (b) The DET index. (c) The ENTR index.
Figure 6
Figure 6
Comparison between the RR method and the spectral analysis based methods for the BSPs detection. (a) A burst suppression interval EEG signal of 80s, (b) frequency spectrum, (c) spectral edge frequency 95 parameter, (d) median electroencephalogram frequency parameter, (e) the RR index, and (f) the BS index of RR.
Figure 7
Figure 7
The bispectrum of burst, suppression, and normal states, respectively.
Figure 8
Figure 8
The boxplot of two different indexes at the burst, suppression, and normal states. (a) Approximate entropy. (b) The RR index.
Figure 9
Figure 9
(a) The long-term EEG recordings with burst suppression patterns. (b) The observation of RR over the entire EEG recordings. (c) Suppression is represented with 0 and burst with 1 to obviously distinguish the two states. (d) The BSR is calculated.

References

    1. Schaul N. The fundamental neural mechanisms of electroencephalography. Electroencephalography and Clinical Neurophysiology. 1998;106(2):101–107.
    1. van Putten MJAM, van Putten MHPM. Uncommon EEG burst-suppression in severe postanoxic encephalopathy. Clinical Neurophysiology. 2010;121(8):1213–1219.
    1. Zhang D, Jia X, Ding H. The effect of anesthetic concentration on burst-suppression of the EEG in rats. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2012;29(2):223–228.
    1. Ching S, Purdon PL, Vijayand S, Kopelld NJ, Brow EN. A neurophysiological-metabolic model for burst suppression. Proceedings of the National Academy of Sciences of the United States of America. 2012;109(8):3095–3100.
    1. Schack B, Witte H, Helbig M, Schelenz C, Specht M. Time-variant non-linear phase-coupling analysis of EEG burst patterns in sedated patients during electroencephalic burst suppression period. Clinical Neurophysiology. 2001;112(8):1388–1399.
    1. Akrawi WP, Drummond JC, Kalkman CJ, Patel PM. A comparison of the electrophysiologic characteristics of EEG burst-suppression as produced by isoflurane, thiopental, etomidate, and propofol. Journal of Neurosurgical Anesthesiology. 1996;8(1):40–46.
    1. Yoon JR, Kim YS, Kim TK. Thiopental-induced burst suppression measured by the bispectral index is extended during propofol administration compared with sevoflurane. Journal of Neurosurgical Anesthesiology. 2012;24(2):146–151.
    1. Muthuswamy J, Sherman DL, Thakor NV. Higher-order spectral analysis of burst patterns in EEG. IEEE Transactions on Biomedical Engineering. 1999;46(1):92–99.
    1. Bruhn J, Ropcke H, Rehberg B, Bouillon T, Hoeft A. Electroencephalogram approximate entropy correctly classifies the occurrence of burst suppression pattern as increasing anesthetic drug effect. Anesthesiology. 2000;93(4):981–985.
    1. Sherman DL, Brambrink AM, Walterspacher D, Dasika VK, Ichord R, Thakor NV. Detecting EEG bursts after hypoxic-ischemic injury using energy operators. Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; November 1997; pp. 1188–1190.
    1. Lapedes A, Farber R. Nonlinear Signal Processing Using Neural Networks: Prediction and System Modelling. 1987.
    1. Kaiser JF. On a simple algorithm to calculate the “energy” of a signal. Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP '90); 1990; IEEE;
    1. Trulla LL, Giuliani A, Zbilut JP, Webber CL., Jr. Recurrence quantification analysis of the logistic equation with transients. Physics Letters, Section A: General, Atomic and Solid State Physics. 1996;223(4):255–260.
    1. Zbilut JP, Giuliani A, Webber CL., Jr. Detecting deterministic signals in exceptionally noisy environments using cross-recurrence quantification. Physics Letters A: General, Atomic and Solid State Physics. 1998;246(1-2):122–128.
    1. Zbilut JP, Giuliani A, Webber CL., Jr. Recurrence quantification analysis and principal components in the detection of short complex signals. Physics Letters A: General, Atomic and Solid State Physics. 1998;237(3):131–135.
    1. Marino AA, Nilsen E, Frilot C. Nonlinear changes in brain electrical activity due to cell phone radiation. Bioelectromagnetics. 2003;24(5):339–346.
    1. Marwan N, Kurths J, Saparin P. Generalised recurrence plot analysis for spatial data. Physics Letters A: General, Atomic and Solid State Physics. 2007;360(4-5):545–551.
    1. Faure P, Korn H. A new method to estimate the Kolmogorov entropy from recurrence plots: its application to neuronal signals. Physica D: Nonlinear Phenomena. 1998;122(1–4):265–279.
    1. Thomasson N, Hoeppner TJ, Webber CL, Jr., Zbilut JP. Recurrence quantification in epileptic EEGs. Physics Letters A: General, Atomic and Solid State Physics. 2001;279(1-2):94–101.
    1. Marwan N, Wessel N, Meyerfeldt U, Schirdewan A, Kurths J. Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. Physical Review E: Statistical, Nonlinear, and Soft Matter Physics. 2002;66(2, part 2)026702
    1. Sleigh JW, Leslie K, Voss L. The effect of skin incision on the electroencephalogram during general anesthesia maintained with propofol or desflurane. Journal of Clinical Monitoring and Computing. 2010;24(4):307–318.
    1. Leslie K, Sleigh J, Paech MJ, Voss L, Lim CW, Sleigh C. Dreaming and electroencephalographic changes during anesthesia maintained with propofol or desflurane. Anesthesiology. 2009;111(3):547–555.
    1. Krishnaveni V, Jayaraman S, Anitha L, Ramadoss K. Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. Journal of Neural Engineering. 2006;3(4):338–346.
    1. Mitra SK, Kuo Y. Digital Signal Processing: A Computer-Based Approach. Vol. 2. New York, NY, USA: McGraw-Hill; 2006.
    1. Takens F. Dynamical systems and turbulence. Proceedings of a Symposium Held at the University of Warwick; 1981;
    1. Marwan N. Encounters With Neighbours: Current Developments of Concepts Based on Recurrence Plots and Their Applications. 2003.
    1. Eckmann JP, Kamphorst SO, Ruelle D. Recurrence plots of dynamical systems. Europhysics Letters. 1987;4:p. 973.
    1. Hartikainen K, Rorarius M, Makela K, Perakyla J, Varila E, Jantti V. Visually evoked bursts during isoflurane anaesthesia. British Journal of Anaesthesia. 1995;74(6):681–685.
    1. Grassberger P, Schreiber T, Schaffrath C. Nonlinear time sequence analysis. International Journal of Bifurcation and Chaos. 1991;1(3):521–547.
    1. Provost F, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. Proceedings of the 15th International Conference on Machine Learning (ICML '98); 1998.
    1. Abarbanel HDI, Kennel MB. Local false nearest neighbors and dynamical dimensions from observed chaotic data. Physical Review E. 1993;47(5):3057–3068.
    1. Holzfuss J, Mayer-Kress G. Dimensions and Entropies in Chaotic Systems. 1986. An approach to error-estimation in the application of dimension algorithms; pp. 114–122.
    1. Jiang A-H, Huang X-C, Zhang Z-H, Li J, Zhang Z-Y, Hua H-X. Mutual information algorithms. Mechanical Systems and Signal Processing. 2010;24(8):2947–2960.
    1. Bruhn J, Bouillon TW, Radulescu L, Hoeft A, Bertaccini E, Shafer SL. Correlation of approximate entropy, bispectral index, and spectral edge frequency 95 (SEF95) with clinical signs of “anesthetic depth” during coadministration of propofol and remifentanil. Anesthesiology. 2003;98(3):621–627.
    1. Särkelä M, Mustola S, Seppänen T, et al. Automatic analysis and monitoring of burst suppression in anesthesia. Journal of Clinical Monitoring and Computing. 2002;17(2):125–134.
    1. Marwan N, Carmen Romano M, Thiel M, Kurths J. Recurrence plots for the analysis of complex systems. Physics Reports. 2007;438(5-6):237–329.

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