Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG

Bo Ram Lee, Dong Ok Won, Kwang Suk Seo, Hyun Jeong Kim, Seong Whan Lee

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

1 Citation (Scopus)

Abstract

There have been lots of trials to classify a depth of anesthesia using diverse physiological indices. In this study, we classified wakefulness and propofol-induced sedation using combined electroencephalography (EEG) and electrocardiography (ECG) features for better classification performance. We extract each spectral band of EEG and very low frequency (VLF) of heart rate variability using spectrogram and low-pass filter, respectively. We used combined feature of EEG spectral bands and VLF and shrinkage-regularized linear discriminant analysis as a classifier. Our results show that combination of EEG spectral power and VLF can improve the classification performance between wakefulness and sedation from 95.1±5.3% to 96.4±4.2%.

Original languageEnglish
Title of host publication5th International Winter Conference on Brain-Computer Interface, BCI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages88-90
Number of pages3
ISBN (Electronic)9781509050963
DOIs
Publication statusPublished - 2017 Feb 16
Event5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of
Duration: 2017 Jan 92017 Jan 11

Other

Other5th International Winter Conference on Brain-Computer Interface, BCI 2017
CountryKorea, Republic of
CityGangwon Province
Period17/1/917/1/11

Fingerprint

Anesthetics
Electroencephalography
Electrocardiography
Low pass filters
Discriminant analysis
Classifiers

Keywords

  • Electrocardiography (ECG)
  • Electroencephalography (EEG)
  • Propojol
  • Sedation
  • Sigma frequency power
  • Very low frequency (VLF)

ASJC Scopus subject areas

  • Signal Processing
  • Human-Computer Interaction

Cite this

Lee, B. R., Won, D. O., Seo, K. S., Kim, H. J., & Lee, S. W. (2017). Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017 (pp. 88-90). [7858168] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWW-BCI.2017.7858168

Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG. / Lee, Bo Ram; Won, Dong Ok; Seo, Kwang Suk; Kim, Hyun Jeong; Lee, Seong Whan.

5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 88-90 7858168.

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

Lee, BR, Won, DO, Seo, KS, Kim, HJ & Lee, SW 2017, Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG. in 5th International Winter Conference on Brain-Computer Interface, BCI 2017., 7858168, Institute of Electrical and Electronics Engineers Inc., pp. 88-90, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, Gangwon Province, Korea, Republic of, 17/1/9. https://doi.org/10.1109/IWW-BCI.2017.7858168
Lee BR, Won DO, Seo KS, Kim HJ, Lee SW. Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG. In 5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 88-90. 7858168 https://doi.org/10.1109/IWW-BCI.2017.7858168
Lee, Bo Ram ; Won, Dong Ok ; Seo, Kwang Suk ; Kim, Hyun Jeong ; Lee, Seong Whan. / Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG. 5th International Winter Conference on Brain-Computer Interface, BCI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 88-90
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