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 high-
pass 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.
Keywords— Sleep stage classification, discrete wavelet
transform, EEG signal
I. INTRODUCTION
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
Fig. 1 EEG sleep stages classification
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
TABLE I
Erdem Tuncer/ Bahcecik Vocational and Technical Anatolian High School
Kocaeli University, Turkey. Emailid: [email protected]
Emine Dogru Bolat/ Kocaeli University, Technical Education Faculty,
Kocaeli University, Turkey. Email id: [email protected].
TECHNIQUES USED FOR SLEEP CLASSIFICATION [1]
Author Year Feature
Extraction Classification
Schmitt,R.B., et al. 1998 Fourier
Transform HMM1
Heiss, J.E., et al. 2002 - Neuro-Fuzzy
Subasi,A., et al. 2005 Discrete WT Neural Network
Kerkeni. N. 2005 Fourier
Transform Neural Network
Doroshenkov, L.G., et
al. 2007
Fourier
Transform HMM
Tang, W.C., et al. 2007 HTT+WT SVM
Ebrahimi,F., et al. 2008 Wavelet Packet Neural Network
Liu,H.J.,et al 2010 Fourier
Transform SVM2
Vatankhah,M., et al. 2010 Discrete WT SVM+NF4
Ouyang T.,Lu,H.T. 2010 Continuous WT SWM
Liu,Y., et al. 2010 HHT3 Neural Network
Le Quoe Khai,Truong
Quang Dang
Khoa.et[2]
2011 FFT Hierarchical
Manner
Ms.Vijaylaxmi.P.Jain,
Dr.V.D.Mytri.
et.al.[3]
2012
Discrete
Wavelet
Transform
Neural Network
Guohun Zhu, Yan
Li[4] 2013
Mapped into a
VG5 and a
HVG6
SVM
Khald
A.l.Aboalayon,Helen
T.et.al.[5]
2014
Statistical
Features
Extraction
SVM
Marwa Obayya
F.E.Z.Abou-Chadil[6] 2014
Spectral and
Wavelet
Analyses
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
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-
EEG Signal Based Sleep Stage Classification
Using Discrete Wavelet Transform
Erdem Tuncer, and Emine Dogru Bolat
S
Input EEG
signalPre-processing
Feature
ExtractionClassification
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
http://dx.doi.org/10.17758/IAAST.A1014055 57
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. TABLE II
THE EEG SPECTRUM [7],[8]
Type of the EEG
Signal Band Frequency in Hz
Beta > 13 Hz
Alpha 8 – 13 Hz
Theta 3 – 7 Hz
Delta < 4 Hz
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
T3
T5
FZ
CZ
PZ
F3
C3
P3
F8
T4
T6
F4
C4
P4
O1 O2
Fig. 2 According to the international 10-20 system, 19-channel
electrode placement. Occipital electrode placements are shown with
red color [9]
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
T3
T5
FZ
CZ
PZ
F3
C3
P3
F8
T4
T6
F4
C4
P4
O1 O2
Fig. 3 According to the international 10-20 system, 19-channel
electrode placement. Frontal electrode placements are shown with
red color [9]
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].
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].
III. SLEEP STAGES
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. 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].
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].
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].
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
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
http://dx.doi.org/10.17758/IAAST.A1014055 58
wakefulness. So, the person in this stage is sensitive to the
noise or movements around and may wake up at any time.
IV. THE TECHNIQUES USED IN SLEEP EEG
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].
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.
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].
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 high-
pass filter gives the Detail Coefficients (D) and the output of
the low-pass filter gives the Approximate coefficients (A). [1],
[16],[17]
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].
S
D1A1
DA2AA2 DD2AD2
Fig. 5 Wavelet decomposition tree [18]
V. WAVELET BASED SLEEP STAGE ANALYSIS AND
SIMULATIONS
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.
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.
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. TABLE III
WAVELET SUB-BANDS
Wavelet Transform (Hz) Type of Activity
0 – 3,125 Delta
3,125 – 6,25 Theta
6,25 – 12,5 Alpha
12,5 - 50 Beta
S
Low pass High pass
A D
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
http://dx.doi.org/10.17758/IAAST.A1014055 59
Fig. 6 Discrete wavelet decomposition
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.
TABLE IV
FEATURE EXTRACTION FOR SLEEP STAGES
Sleep
Stages Statistical Properties
NREM3
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
WAKE
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
0.6928
-0.5581
0.113939
0.127990
0.232222
0.525845
NREM1
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
27.4449
-21.7651
0.417746
0.187510
0.200229
0.194515
REM
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
31.5828
-21.9715
0.473648
0.275365
0.210219
0.038767
NREM2
Max Ampl.
Min Ampl.
Delta Energy/Total Energy
Theta Energy/Total Energy
Alpha Energy/Total Energy
Beta Energy/Total Energy
66.2343
-66.6280
0.729521
0.160600
0.083454
0.026436
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
Amp= abs ( Max amp. - Min amp.)
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.
An epoch of EEG signal
4 * E9 <= E1 NREM 3Yes
E7 or E8 > E6 WAKE
If E5
bigger than others,
E6 <E5/2 and
E5/2 – E6 < 0.05
NREM 1
If E5
bigger than others,
E6 > E5/2
Amplitude>115µV
REM
If E6
bigger than others
and
E6 – E5 >0.15
NREM 2
Yes
Yes
Yes Yes
Yes
No
No
No
No
No
No
Fig. 7 The flow chart diagram
TABLE V
PERFORMANCE RESULT FOR SUBJECT 19
International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
http://dx.doi.org/10.17758/IAAST.A1014055 60
Category Number of Tested
signals
Correctly
Detected
Accuracy
(%)
NREM 3 30 30 ~100
WAKE 55 44 ~80
NREM 1 25 8 ~32
NREM 2 20 15 ~75
REM 55 39 ~70
TABLE VI
PERFORMANCE RESULT FOR SUBJECT 20
Category Number of Tested
signals
Correctly
Detected
Accuracy
(%)
NREM 3 30 29 ~96
WAKE 55 50 ~90
NREM 1 32 12 ~37
NREM 2 43 33 ~76
REM 55 45 ~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 electro-
oculography (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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International Conference on Chemistry, Biomedical and Environment Engineering (ICCBEE'14) Oct 7-8, 2014 Antalya (Turkey)
http://dx.doi.org/10.17758/IAAST.A1014055 61
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