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 | |
| 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 |
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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
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
}
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.
ER -