Permutation entropy for discriminating 'conscious' and 'unconscious' state in general anesthesia

Nicoletta Nicolaou, Saverios Houris, Pandelitsa Alexandrou, Julius Georgiou

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Brain-Computer Interfaces (BCIs) are devices offering alternative means of communication when conventional means are permanently, or nonpermanently, impaired. The latter is commonly induced in general anesthesia and is necessary for the conduction of the surgery. However, in some cases it is possible that the patient regains consciousness during surgery, but cannot directly communicate this to the anesthetist due to the induced muscle paralysis. Therefore, a BCI-based device that monitors the spontaneous brain activity and alerts the anesthetist is an essential addition to routine surgery. In this paper the use of Permutation Entropy (PE) as a feature for 'conscious' and 'unconscious' brain state classification for a BCI-based anesthesia monitor is investigated. PE is a linear complexity measure that tracks changes in spontaneous brain activity resulting from the administration of anesthetic agents. The overall classification performance for 10 subjects, as assessed with a linear Support Vector Machine, exceeds 95%, indicating that PE is an appropriate feature for such a monitoring device.

Original languageEnglish
Title of host publicationEngineering Applications of Neural Networks - 12th INNS EANN-SIG International Conference, EANN 2011 and 7th IFIP WG 12.5 International Conference, AIAI 2011, Proceedings
Pages280-288
Number of pages9
EditionPART 1
DOIs
Publication statusPublished - 2 Nov 2011
Externally publishedYes
Event12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011 - Corfu, Greece
Duration: 15 Sept 201118 Sept 2011

Publication series

NameIFIP Advances in Information and Communication Technology
NumberPART 1
Volume363 AICT
ISSN (Print)1868-4238

Conference

Conference12th INNS EANN-SIG International Conference on Engineering Applications of Neural Networks, EANN 2011
Country/TerritoryGreece
CityCorfu
Period15/09/1118/09/11

Keywords

  • anesthesia monitor
  • Brain-Computer Interface
  • electroencephalogram
  • Permutation Entropy
  • Support Vector Machine

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