International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
EEG Signal Based Sleep Stage Classification
Using Discrete Wavelet Transform
Erdem Tuncer, and Emine Dogru Bolat

TECHNIQUES USED FOR SLEEP CLASSIFICATION [1]
Abstract—A single channel EEG signal based sleep stage
classification using Discrete Wavelet Transform (DWT) is aimed in
this study. DWT is applied to 30-second epochs of the EEG
recordings. Recordings from Dreams Project database are used in this
study. The EEG signal is filtered by Butterworth low-pass and highpass filters first. Then, it is decomposed into five sub-bands using
DWT according to the American Academy of Sleep Medicine
(AASM) standards. Epochs are selected randomly and classified
using the presented algorithm. The obtained results are compared
with the results scored by an expert of dreams Project internet site.
Year
Feature
Extraction
Classification
1998
Fourier
Transform
HMM1
Heiss, J.E., et al.
2002
-
Neuro-Fuzzy
Subasi,A., et al.
2005
Discrete WT
Fourier
Transform
Fourier
Transform
Neural Network
Author
Schmitt,R.B.,
et al.
Kerkeni. N.
2005
Doroshenkov, L.G., et
al.
2007
Tang, W.C., et al.
2007
HTT+WT
SVM
I. INTRODUCTION
Ebrahimi,F., et al.
2008
Neural Network
LEEP is a basic need for a human being’s mental and
physiological recovery and covering almost one third
period of a daytime. A quality and deep sleep is required
for efficient regeneration of the body. Sleep stages arise with
the evaluation of the quality and deep sleep. EEG signal is
commonly used for sleep stage analysis and classification. In
literature, the methods for the analysis and classification of
EEG based sleep stages are composed of three main steps;
(i) Preprocessing of EEG signal
(ii) Feature extraction from the EEG signal
(iii) Applying extracted features to a classifier
Liu,H.J.,et al
2010
Vatankhah,M., et al.
Ouyang T.,Lu,H.T.
2010
2010
Wavelet Packet
Fourier
Transform
Discrete WT
Continuous WT
Liu,Y., et al.
2010
HHT3
Neural Network
2011
FFT
Hierarchical
Manner
Keywords— Sleep stage classification, discrete wavelet
transform, EEG signal
S
Input EEG
signal
Pre-processing
Feature
Extraction
Le Quoe Khai,Truong
Quang Dang
Khoa.et[2]
Ms.Vijaylaxmi.P.Jain,
Dr.V.D.Mytri.
et.al.[3]
Classification
Fig. 1 EEG sleep stages classification
Guohun Zhu, Yan
Li[4]
2013
Khald
A.l.Aboalayon,Helen
T.et.al.[5]
2014
Marwa Obayya
F.E.Z.Abou-Chadil[6]
2014
Discrete
Wavelet
Transform
Mapped into a
VG5 and a
HVG6
Statistical
Features
Extraction
Spectral and
Wavelet
Analyses
HMM
SVM2
SVM+NF4
SWM
Neural Network
SVM
SVM
Fuzzy C-Means
Algorithm
1
Hidden Markov Model 2 Support Vector Machine 3 Hilbert Huang
Transform 4 NeuroFuzzy 5 Visibility Graph 6 Horizontal Visibility
Graph
The block diagram of the three steps is illustrated in Fig. 1.
In Pre-processing stage, processes such as filtering the signal
from the distortions and normalizing are realized. Important
distinctive features of the signal are obtained in
Feature Extraction Stage. Studies in literature show three
main groups of extracted features as given below.
1- Features obtained in time domain
2- Features obtained in the frequency domain
3- Features obtained both in time and frequency domain
In the last stage, Classification, the results are obtained using
the algorithm based on the extracted features.
Some studies about the classification of sleep stages from
1998 up to now are given in TABLE I. Feature Extraction and
Classification methods are also stated in this TABLE.
In this study, EEG signal is decomposed into sub-bands
using discrete wavelet transform. The features of these sub-
TABLE I
Erdem Tuncer/ Bahcecik Vocational and Technical Anatolian High School
Kocaeli University, Turkey. Emailid: erdemtuncerr@gmail.com
Emine Dogru Bolat/ Kocaeli University, Technical Education Faculty,
Kocaeli University, Turkey. Email id: ebolat@gmail.com.
http://dx.doi.org/10.17758/IAAST.A1014055
2012
Neural Network
57
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
C. Theta Waves
Theta waves are seen in the people about to sleep or in the
first stages of the sleep. They are the waves between 3-7 Hz.
Their amplitude is smaller than 100 μVpp [7], [9], [10].
bands are extracted and classification is realized using these
features.
II. ELECTRICAL CHARACTERISTICS OF THE EEG SIGNAL
The EEG signal frequency band used to classify sleep
stages is between 0.5-35 Hz. Amplitude, phase and frequency
values of EEG signal change with time continuously. EEG
signal is analyzed in four signal bands named as Beta, Alpha,
Theta and Delta. TABLE II shows the type of EEG signal band
and the frequency intervals the signal bands include.
D. Delta Waves
Delta waves occur in people sleeping deep. They are the
brain waves between 0.5-4 Hz. Their amplitude is smaller than
100 μVpp. They are recorded from the frontal region mostly
[7], [9],[10].
TABLE II
III. SLEEP STAGES
THE EEG SPECTRUM [7],[8]
Type of the EEG
Signal Band
Frequency in Hz
Beta
Alpha
Theta
Delta
> 13 Hz
8 – 13 Hz
3 – 7 Hz
< 4 Hz
Up to the recent past, the sleep stages have been scored
using the Rechtschaffen and Kales (R&K) scoring criteria set
up in 1968. According to this criterion, five sleep stages were
described as Non-Rapid Eye Movement (NREM) 1, 2, 3, 4
and Rapid Eye Movement (REM). American Academy of
Sleep Medicine (AASM) established new rules about scoring
the sleep stages in 2007. These rules are based on today.
According to these rules;
A- The sleep stages are composed of wake (W), stage I
(N1), stage II (N2), stage III (N3) and REM (R).
(NREM 4 is removed from sleep terminology.)
B- Sleep is scored according to the epochs.
C- 30 second epochs are required at most for scoring the
sleep stages.
D- Each epoch is named by a stage. If two stages appear
in the same epoch, it is named by the stage covering
more than half of the epoch. [7],[8]
A. Alpha Waves
Alpha rhythm is observed in awake (normal), relax, calm
and resting people with closed eyes. They include the waves
between 8-13 Hz. It is observed in the occipital region
intensively [7], [9], [10].
FP1
FP2
F7
F3
FZ
F4
F8
T3
C3
CZ
C4
T4
T5
P3
PZ
P4
T6
O1
A. Stage W (WAKE)
If more than half of the epoch is the alpha wave (8-13 Hz),
it is relaxed wakefulness with closed eyes. If it is beta wave
(+13 Hz), it is the sign of active wakefulness with open eyes.
The existence of the rapid eye movement is the sign of the
wakefulness while alpha waves are not apparent [7], [8].
O2
Fig. 2 According to the international 10-20 system, 19-channel
electrode placement. Occipital electrode placements are shown with
red color [9]
B. Stage N1 (NREM-1)
Theta activity between 4-7 Hz is dominant at this stage.
Vertex sharp waves can be seen. The existence of more than
minimum 0.5 second eye movement is the sign of NREM-1
stage [7], [8].
B. Beta Waves
Beta waves are observed in people with the conditions of
active thinking, concentration, solution of daily problems
when their eyes are open. They include the brain waves with
the frequencies greater than 13 Hz. It is recorded from the
frontal region specifically [7],[9],[10].
FP1
FP2
F7
F3
FZ
F4
F8
T3
C3
CZ
C4
T4
T5
P3
PZ
P4
T6
O1
C. Stage N2 (NREM-2)
Sleep spindles and K complex exist as the signs of this
stage. K complex is the waves, including negative deflection,
followed by a positive component. Sleep spindles are 12-14
Hz and minimum 0.5 s. episodic bursts [3], [7], [8].
D. Stage N3 (NREM-3)
This stage has a frequency between 0.5-2 Hz. It is the most
relaxing stage. Sleepiness condition occurs with the lack of
this stage during the day [7], [8].
O2
Fig. 3 According to the international 10-20 system, 19-channel
electrode placement. Frontal electrode placements are shown with
red color [9]
http://dx.doi.org/10.17758/IAAST.A1014055
E. Stage R (REM)
Maximum 2-6 Hz, sharp-pointed saw tooth waves like
triangle and more than minimum 0.5 sec. slow eye movement
occur in this stage [8]. The theta activity is dominant as in the
stage NREM-1 [7]. REM is the nearest stage to the
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International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
wakefulness. So, the person in this stage is sensitive to the
noise or movements around and may wake up at any time.
S
IV. THE TECHNIQUES USED IN SLEEP EEG
A1
A. Fourier Analyze
The Fourier analyze is a proper method since it gives the
opportunity to work with the meaningful frequencies for the
signals, carrying the signal from the time domain to the
frequency domain. Occurrence of exceptional waves in the
nonstationary signals such as EEG is important. Fourier
analyze is insufficient in this case [11], [12].
AA2
DA2
AD2
DD2
Fig. 5 Wavelet decomposition tree [18]
V. WAVELET BASED SLEEP STAGE ANALYSIS AND
SIMULATIONS
B. Wavelet Transformation
Optimum time-frequency resolution can be provided at all
frequency intervals because of the variable window sizes [13].
Therefore, it becomes more appropriate to analyze the
nonstationary signals using wavelet analysis [10], [11].
Wavelet transformation can be collected under 3 subtitles.
A. Data Collection
Sleep EEG signals are taken from the subject19.edf and
subject20.edf recordings and CZA1 channel
on
dreamsproject.net internet site. The sampling frequency is 200
Hz. Text files including scored data by an expert considering
the AASM standards are taken as reference for scoring. The
EEG signal is divided into 30 s windows and scoring is
realized for each window.
B1. The Continuous Wavelet Transform
A wavelet is a time-localized wave having an average zero
value [14]. Searched wavelet on the signal is found by scaling
the obtained wavelet in time-scale axis, shifting the obtained
wavelet on the processed signal and regarding the correlation
value [15], [16].
B. Preprocessing
The sleep EEG signal is passed through the 6.degree
Butterworth high-pass filter and 16.degree low-pass filter for
the frequencies below 35 Hz. In other words, the sleep EEG
signal is prepared to be processed excluding the frequencies
between 0.5-35 Hz. Designing a higher filter using filters
separately is observed more appropriate than designing a
band-pass filter according to the simulation studies.
B2. Discrete Wavelet Transform
The original signal is passed through the complementary
high and low pass filters. This process can be repeated until
reaching the desired frequency range. The output of the highpass filter gives the Detail Coefficients (D) and the output of
the low-pass filter gives the Approximate coefficients (A). [1],
[16],[17]
C. Wavelet Transform
The EEG signal is decomposed into five sub-bands to
obtain alpha, beta, theta and gamma bands using discrete
wavelet transform. Daubechies 44 (Db44) wavelet from
Orthogonal Wavelets family is used and the sub-bands are
illustrated in TABLE III.
S
Low pass
D1
High pass
TABLE III
WAVELET SUB-BANDS
A
Wavelet Transform (Hz)
0 – 3,125
3,125 – 6,25
6,25 – 12,5
12,5 - 50
D
Fig. 4 Sign of the low-and high-pass filter outputs [18]
A signal having 200 Hz sampling frequency includes
frequency components between 0-100 Hz range according to
the Nyquist Criterion. Thus, approximate (A) coefficients
gives the frequency components between 0-50 Hz and detail
(D) coefficients gives frequency components between 50-100
Hz.
B3. Wavelet Packet Transform
Both the detail (D) and approximate (A) coefficients are
decomposed into sub-bands in the wavelet packet transform
while only approximate (A) coefficients are decomposed into
sub-bands in the discrete wavelet transform. Therefore, the
wavelet packet transform enables more detailed signal
processing [15], [16].
http://dx.doi.org/10.17758/IAAST.A1014055
59
Type of Activity
Delta
Theta
Alpha
Beta
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
Fig. 6 Discrete wavelet decomposition
Amp= abs ( Max amp. - Min amp.)
D. Feature Selection
Statistical features decomposed from an epoch of EEG
signal:
i: Minimum (min) amplitude
ii: Total energy
iii: Maximum (max) amplitude
iv: Energy values calculated for five sub-bands obtained using
discrete wavelet transform (delta, theta, alpha, beta energy)
v: the value obtained by dividing the calculated energy values
for sub-bans by total energy (Delta/Total Energy etc.)[3]
The extracted Features are shown in TABLE IV.
In the first step, E5 ratio of a 30 s epoch of EEG signal is
calculated. If this ratio is minimum 4 times of the biggest of
the other ratios (E6, E7, E8), this epoch is scored as NREM-3.
In the second step, the energy value of E7 and E8 bands are
examined. If one of these two values is bigger than E6 value,
this epoch is scored as WAKE. In the third step, E5 ratio is
high, however E6 value is less than half of the E5 value and
nearer than 0.05 to E5 value, it is scored as NREM-1. In the
fourth step, if E6 ratio is bigger than half of the E5 ratio, we
examine the amplitude of the epoch. If the condition is
satisfied, the epoch is scored as REM. If the condition is not
satisfied, the epoch is scored as NREM-2. In the last step, if
the E6 ratio is bigger than the other ratio values (E5, E7, E8)
and the E9 ratio value is higher than 0.15, the epoch is scored
as NREM-2. Scoring is applied to the randomly selected
epochs for each sleep stage and the results are given in
TABLE V and VI.
TABLE IV
FEATURE EXTRACTION FOR SLEEP STAGES
Sleep
Stages
NREM3
WAKE
NREM1
REM
NREM2
Statistical Properties
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
63.6248
-101.5187
0.881157
0.078908
0.030490
0.009445
0.6928
-0.5581
0.113939
0.127990
0.232222
0.525845
27.4449
-21.7651
0.417746
0.187510
0.200229
0.194515
31.5828
-21.9715
0.473648
0.275365
0.210219
0.038767
66.2343
-66.6280
0.729521
0.160600
0.083454
0.026436
An epoch of EEG signal
Yes
4 * E9 <= E1
NREM 3
No
Yes
E7 or E8 > E6
WAKE
No
If E5
bigger than others,
E6 <E5/2 and
E5/2 – E6 < 0.05
Yes
NREM 1
No
Yes
If E5
bigger than others,
E6 > E5/2
Yes
Amplitude>115µV
No
If E6
bigger than others
and
E6 – E5 >0.15
E. Classification
The flow chart given in Fig. 7 is utilized for classification
of sleep stages. Statistical abbreviations calculated for an
epoch are given below:
ET= Total energy
E1= Energy in Delta Band
E2= Energy in Theta Band
E3= Energy in Alpha Band
E4= Energy in Beta Band
E5= Ratio of energy in Delta and ET
E6= Ratio of energy in Theta and ET
E7= Ratio of energy in Alpha and ET
E8= Ratio of energy in Beta and ET
E9= E6-E5
http://dx.doi.org/10.17758/IAAST.A1014055
No
Yes
NREM 2
No
Fig. 7 The flow chart diagram
TABLE V
PERFORMANCE RESULT FOR SUBJECT 19
60
REM
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
Category
NREM 3
WAKE
NREM 1
NREM 2
REM
Number of Tested
signals
30
55
25
20
55
Correctly
Detected
30
44
8
15
39
[13] Farideh Ebrahimi, Mohammad Mikaeili, Edson Estrada, Homer Nazeran,
“Automatic Sleep Stage Classification Based on EEG Signals by Using
Neural Networks and Wavelet Packet Coefficients”, 30th Annual
International IEEE EMBS Conference, August 20-24, 2008.
http://dx.doi.org/10.1109/IEMBS.2008.4649365
[14] Cagri Kocaman, Muammer Ozdemir, “Dalgacik Yaklasiminin ve
Dalgacik Katsayilarindan Enerji Yontemiyle Ozellik Cikarimi
Yontemlerinin Bazı Guc Kalitesi Bozucularının Belirlenmesinde
Kullanilmasi”.
[15] Michel Misiti,Yves Misiti, Georges Oppenheim, Jean-Michel Poggi,
“Wavelet Toolbox”, March 1996.
[16] Aykut Erdamar, “A model development for predıctıon of sleep apnea
and simulation of genioglossus muscle”, PhD Thesis, 2007.
[17] Huseyin Acar, Mehmet Akin, Abdulnasir Yildiz, Hakkı Egi, Gokhan
Kirbas, “The Classification of Alertness Level from EEG Signals by
Using TMS320C6713 DSK and MATLAB”, IEEE 2010.
[18] Cuneyt Aliustaoglu, H. Metin Ertunc, Hasan Ocak, “Determining
Bearing Faults Using Wavelet and Approximate Entropy”, IEEE 2008.
Accuracy
(%)
~100
~80
~32
~75
~70
TABLE VI
PERFORMANCE RESULT FOR SUBJECT 20
Category
NREM 3
WAKE
NREM 1
NREM 2
REM
Number of Tested
signals
30
55
32
43
55
Correctly
Detected
29
50
12
33
45
Accuracy
(%)
~96
~90
~37
~76
~81
VI. CONCLUSION
In this study, the EEG signal taken from a single channel is
used for classification of the sleep stages. The average success
rate of classification of the subject 19 is obtained as 76%. It is
achieved as 71,4% for the subject 20. The characteristic
features, the K-complex and sleep spindles of NREM-2, will
be determined using Continuous Wavelet Transform and
changes in eye movements will be analyzed using electrooculography (EOG) signals to be able to increase the accuracy
of the classification in future studies. This study will be aimed
to automate by applying the classification to the whole EEG
signal.
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