EEG Signal Based Sleep Stage Classification Using Discrete ...iaast.org/upload/2718A1014055.pdf · classification using Discrete Wavelet Transform ... EEG based sleep stages are composed

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

http://www.aasmnet.org/

http://www.aasmnet.org/

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



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



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.





0.6928

-0.5581

0.113939

0.127990

0.232222

0.525845

NREM1

Max Ampl.

Min Ampl.





27.4449

-21.7651

0.417746

0.187510

0.200229

0.194515

REM

Max Ampl.

Min Ampl.





31.5828

-21.9715

0.473648

0.275365

0.210219

0.038767

NREM2

Max Ampl.

Min Ampl.





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



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.

REFERENCES

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EEG Signal Based Sleep Stage Classification Using Discrete ...iaast.org/upload/2718A1014055.pdf · classification using Discrete Wavelet Transform ... EEG based sleep stages are composed