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

doi:10.1109/IWW-BCI.2017.7858168

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 proceeding › Conference contribution

2 Citations (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 language	English
Title of host publication	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	88-90
Number of pages	3
ISBN (Electronic)	9781509050963
DOIs	https://doi.org/10.1109/IWW-BCI.2017.7858168
Publication status	Published - 2017 Feb 16
Event	5th International Winter Conference on Brain-Computer Interface, BCI 2017 - Gangwon Province, Korea, Republic of Duration: 2017 Jan 9 → 2017 Jan 11

Publication series

Name	5th International Winter Conference on Brain-Computer Interface, BCI 2017

Other

Other	5th International Winter Conference on Brain-Computer Interface, BCI 2017
Country/Territory	Korea, Republic of
City	Gangwon Province
Period	17/1/9 → 17/1/11

Keywords

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

ASJC Scopus subject areas

Signal Processing
Human-Computer Interaction

Access to Document

10.1109/IWW-BCI.2017.7858168

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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, 5th International Winter Conference on Brain-Computer Interface, BCI 2017, 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. (5th International Winter Conference on Brain-Computer Interface, BCI 2017). doi: 10.1109/IWW-BCI.2017.7858168

Lee, Bo Ram ; Won, Dong Ok ; Seo, Kwang Suk et al. / 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 (5th International Winter Conference on Brain-Computer Interface, BCI 2017).

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title = "Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG",

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\%.",

keywords = "Electrocardiography (ECG), Electroencephalography (EEG), Propojol, Sedation, Sigma frequency power, Very low frequency (VLF)",

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AU - Won, Dong Ok

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AU - Lee, Seong Whan

PY - 2017/2/16

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N2 - 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%.

AB - 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%.

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ER -

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

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Keywords

ASJC Scopus subject areas

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