Permutation entropy: A reliable measure for automatic monitoring of anesthetic depth during surgery?

N. Nicolaou, S. Hourris, P. Alexandrou, J. Georgiou

Research output: Contribution to journalArticlepeer-review

Abstract

Permutation Entropy (PE) has recently been applied to characterize anesthetic-induced changes in the frontal electrical brain activity (EEG) during anesthesia. In this work we investigate the stability of PE as a means of identifying between the awake and anesthetized EEG over the entire duration of surgery under different anesthetic regimes and using a full set of EEG sensors. Average classification rates from 22 patients range between 98-99% (specificity, sensitivity and accuracy), when using information from whole-head EEG. The findings support the robustness of PE for discriminating 'awake' and 'anesthesia' throughout the entire surgery, independently of the anesthetic regime followed.

Original languageEnglish
Pages (from-to)9-18
Number of pages10
JournalEngineering Intelligent Systems
Volume20
Issue number1-2
Publication statusPublished - 1 Mar 2012
Externally publishedYes

Keywords

  • Anesthesia depth monitoring
  • Electroencephalogram
  • Permutation entropy
  • Support vector machine

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