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3), ‚
Abstract ² The purpose of this paper is to classify sleep stages using frequency components of electroencephalography(EEG) signals and time components of electrocardiography(ECG) signals. We used support vector machine(SVM) to analyze sleep stages. As a result, a combination of EEG and ECG signals showed better performance as features for analyzing sleep stages.
I.
I NTRODUCTION
Sleep is a state that does not react to the surrounding environment and reduces feeling. Also, sleep is divided into several sleep stage according to EEG signals. During non-REM sleep, EEG signals are gradually getting low frequency bands from alpha to delta, and people fall into deep sleep stage. REM sleep stage appears after non-REM sleep stage. REM sleep showed mixed patterns of all frequencies and related to movement of eyes[1].
Biomedical signals are important to analyze sleep stage and EEG signals are specially represented useful signals.
Frequency domain analysis of EEG signals becomes principal features because frequency bands differ from the sleep stages.
ECG signals are also used to classify sleep stages because awakening and REM stage have similar frequency domain features. Therefore, we wanted to increase performance of sleep stage classification using two kinds of biomedical signals.
FFT. After FFT, we calculated frequency power and then normalized the frequency power for extracting feature vectors.
ECG signals were used to extract time domain parameters.
Using R peak of ECG signals, we obtained HRV and calculated mean, standard deviation, etc. These time domain parameters were used feature vectors with EEG frequency feature vectors[2].
After extracting feature vectors, we used SVM to classify sleep stage. SVM is better for generalization ability than other classifiers[3]. We set kernel function and slack variable for good performance, than we could know accuracy of sleep stage classification.
III.
C ONCLUSIONS
In this paper, accuracy of sleep stage classification was extract by EEG and ECG signals. We could find that a combination of EEG and ECG signals for features has better performance than only using EEG signals for features.
Specially, awakening state has better performance than other states, so it makes accuracy of sleep stage classification go higher. Table 1 is shown accuracies for each of sleep stage.
II.
M ETHODS
EEG and ECG signals were obtained during 6.5 hours of sleep. We separated the EEG signals for 30 seconds based on the rules of Rechtschaffen and Kales. It was divided into 4 stages such as awake, light sleep, deep sleep and REM stage for supervised learning and they also separated training samples and testing samples, based these 4 stages.
EEG signals have a big correlation with frequency domain, so it could be possible to predict EEG frequency components through FFT. But EEG signals cannot be predicted and are affected from noise a lot. Accordingly pre-processing is important to use EEG signals. So we applied baseline correction, detrending and filtering to EEG signals before
*Resrach supported by the National Research Foundation of Korea(NRF).
JeeEun Lee
1)
, HyeJin Kim
1)
, Byuck jin LEE
1) is with the Graduate School of Biomedical Engineering, Yonsei University, Seoul, Korea (e-mail: wldmsdlt5@gmail.com, dazzlen@naver.com, bjlee1397@gmail.com).
Chungki Lee
2)
is with the Bionics Research Group, Korea Institute of
Science and Technology, Seoul, Korea (e-mail: dr.chungki.lee@gmail.com).
Sun K. Yoo
3), ‚
is with the Department of Medical Engineering, the
College of Medicine, Yonsei University, Seoul, Korea (corresponding author to provide phone: 82-2-2228-1919; fax: 82-2-363-9923; e-mail: sunkyoo@ yuhs.ac).
TABLE I.
EEG
CLASSIFICATION ACCURACY OF SLEEP STAGE
Awake
66.7%
Light
Sleep
91.0%
Deep
Sleep
83.8%
REM
93.7%
EEG&ECG 77.8% 91.0% 84.8% 93.0%
But, in this study, every biomedical signal was used for features, so we could not confirm which parameters are more important. Therefore, we have to detect important features as analysis each of parameters of biomedical signals in further research.
A CKNOWLEDGMENT
This work was supported by the Ministry of Knowledge
Economy(MKE) (10031993) and the National Research
Foundation of Korea(NRF) grant funded by the Korea government(MEST) (No.2010-0026833)
R EFERENCES
[1] Macmillan Dictionary for Students Macmillan, Pan Ltd, pp.936. 1981.
[2] A. John Camm, Marek Malik, ³ Heart rate variability, ´ European Heart
Journal , volume.17, pp.354~381, 1996.
[3] Christopher J.C. Burges, F A Tutorial on Support Vector Machines for Pattern Recognition G , Data Mining and Knowledge Discovery 2, p.121-167, 1998.