EEG-based classification of consciousness during sedation using global spectra principal components

Seul Ki Yeom, Hwi Jae Kim, Kwang Suk Seo, Hyun Jeong Kim, Seong Whan Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of sedation is to maintain patient safety, and reduce the anxiety or pain during surgical procedure. In this point of view, method for monitoring the depth of anesthesia (DOA) should be reliable. Previous electroencephalogram (EEG) based DOA studies under general anesthesia (GA) have shown the significant correlation between brain state and neurophysiological characteristics. However, no matter how many existing DOA studies are under GA environment which is considered as 'the deepest sedation', it could not clearly distinguish between consciousness and unconsciousness during sedation. In this paper, we proposed a novel feature extraction technique, called global spectra principal component (GSPC) motivated by global field synchrony (GPS), using channel-wise coefficients from multi-dimensional channels in interest frequency ranges. As a result, average classification performance of 25 subjects represented 98.7±2.1%. It showed that the proposed method was an efficient feature extraction technique for classification of 'consciousness' and 'unconsciousness' even during sedation.

Original languageEnglish
Title of host publicationProceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages804-809
Number of pages6
ISBN (Electronic)9781538633540
DOIs
Publication statusPublished - 2018 Dec 13
Event4th Asian Conference on Pattern Recognition, ACPR 2017 - Nanjing, China
Duration: 2017 Nov 262017 Nov 29

Other

Other4th Asian Conference on Pattern Recognition, ACPR 2017
CountryChina
CityNanjing
Period17/11/2617/11/29

Keywords

  • Auditory stimulus
  • Electroencephalograph
  • Sedation
  • Spectral analysis

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Yeom, S. K., Kim, H. J., Seo, K. S., Kim, H. J., & Lee, S. W. (2018). EEG-based classification of consciousness during sedation using global spectra principal components. In Proceedings - 4th Asian Conference on Pattern Recognition, ACPR 2017 (pp. 804-809). [8575925] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACPR.2017.57