Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness

  • Sabina Stefan
  • Barbara Schorr
  • Alex Lopez-Rolon
  • Iris-Tatjana Kolassa
  • Jonathan P. Shock
  • Martin Rosenfelder
  • Suzette Heck
  • Andreas Bender
Original Paper

Abstract

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2–20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use (https://qeeg.wordpress.com)

Keywords

Quantitative EEG Unresponsive wakefulness syndrome Minimally conscious state Outcome prediction Microstate analysis Sequential floating forward selection 

Notes

Acknowledgements

The authors wish to thank patients and their caregivers as well as the Information and Communication Technology High Performance Computing Team of the University of Cape Town. Part of this study was supported by Grant 2011013 of the Hannelore-Kohl-Stiftung, and the Deutsche Stiftung Neurologie.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in the present study involving human participants were approved by the institutional review board of the University of Munich and were in accordance with the ethical standards of the 1964 Helsinki declaration and its later amendments.

Supplementary material

10548_2018_643_MOESM1_ESM.pdf (300 kb)
Supplemental Material 1 (PDF 300 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sabina Stefan
    • 1
  • Barbara Schorr
    • 2
    • 3
  • Alex Lopez-Rolon
    • 4
  • Iris-Tatjana Kolassa
    • 3
  • Jonathan P. Shock
    • 5
  • Martin Rosenfelder
    • 2
    • 3
  • Suzette Heck
    • 4
  • Andreas Bender
    • 2
    • 4
  1. 1.School of EngineeringBrown UniversityProvidenceUSA
  2. 2.Department of NeurologyTherapiezentrum BurgauBurgauGermany
  3. 3.Clinical and Biological Psychology, Institute of Psychology and EducationUlm UniversityUlmGermany
  4. 4.Department of NeurologyUniversity of MunichMunichGermany
  5. 5.Department of Mathematics and Applied MathematicsUniversity of Cape TownCape TownSouth Africa

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