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

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 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

Other

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

Fingerprint Dive into the research topics of 'Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG'. Together they form a unique fingerprint.

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 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, 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

@inproceedings{4a8a367879054ee0a9c6ac9856f45207,

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)",

author = "Lee, {Bo Ram} and Won, {Dong Ok} and Seo, {Kwang Suk} and Kim, {Hyun Jeong} and Lee, {Seong Whan}",

year = "2017",

month = feb,

day = "16",

doi = "10.1109/IWW-BCI.2017.7858168",

language = "English",

pages = "88--90",

booktitle = "5th International Winter Conference on Brain-Computer Interface, BCI 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "5th International Winter Conference on Brain-Computer Interface, BCI 2017 ; Conference date: 09-01-2017 Through 11-01-2017",

}

TY - GEN

T1 - Classification of wakefulness and anesthetic sedation using combination feature of EEG and ECG

AU - Lee, Bo Ram

AU - Won, Dong Ok

AU - Seo, Kwang Suk

AU - Kim, Hyun Jeong

AU - Lee, Seong Whan

PY - 2017/2/16

Y1 - 2017/2/16

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%.

KW - Electrocardiography (ECG)

KW - Electroencephalography (EEG)

KW - Propojol

KW - Sedation

KW - Sigma frequency power

KW - Very low frequency (VLF)

UR - http://www.scopus.com/inward/record.url?scp=85016047133&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85016047133&partnerID=8YFLogxK

U2 - 10.1109/IWW-BCI.2017.7858168

DO - 10.1109/IWW-BCI.2017.7858168

M3 - Conference contribution

AN - SCOPUS:85016047133

SP - 88

EP - 90

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Winter Conference on Brain-Computer Interface, BCI 2017

Y2 - 9 January 2017 through 11 January 2017

ER -