Automatic sleep stage classification
V.Sheeba
Dept. Electronics and Instrumentation
VJEC, Chemperi, Kerala
sheebav@vjec.ac.in
Marymol Paul
Dept. Electronics and Instrumentation
VJEC, Chemperi, Kerala
Abstract—Electroencephalogram (EEG) is a complex
signal resulting from postsynaptic potentials of cortical
pyramidal cells and an important brain state indicator
with specific state dependent features. It provides
information related to the brain activity based on
measurements of electrical recordings taken on the scalp
of the subjects. EEG spectral analysis is an important
method to investigate the hidden properties and hence
the brain activities. Spectral analysis of sleep EEG signal
provides acute insight into the features of different
stages of sleep which can be utilized to differentiate
between normal and pathological conditions. Sleep
scoring by a human expert is a very time consuming task
and normally require hours to classify a whole night
recording (6 hours). Every 30 seconds epochs are
classified in different sleep stages, according to the
structure of the signal and rules defined by
Rechtschaffen and Kales [11]. This paper describes the
process of extracting features of sleep EEG signals
through the use of Power Spectral Density and Fast
Fourier Transform.
Keywords- Electroencephalogram (EEG), Power Spectral
Density (PSD) , Fast Fourier Transfor (FFT).
I.
INTRODUCTION
The EEG (Electroencephalogram) signal indicates the
electrical activity of the brain. The electrical activity of a
brain (EEG) exhibits significant complex behavior with
strong non-linear, random and non-stationary properties.
The communication in the brain cells take place through
electrical impulses. It is measured by placing the electrodes
on the scalp of the subject. A typical EEG signal, measured
from the scalp, will have amplitude of about 10 μV to 100
μV and a frequency roughly in the range of 0.25 Hz to about
100 Hz. EEG, as a noninvasive testing method, plays a key
role in diagnosing diseases and is useful for both
physiological research and medical application. It helps in
diagnosing many neurological diseases, such as epilepsy,
tumor, cerebrovascular lesions, breathing disorders
associated with sleep, depressions and problems associated
with trauma. It is very difficult to get useful information
Akhil Jose
Dept. Electronics and Instrumentation
VJEC, Chemperi, Kerala
akhiljose@vjec.ac.in
Avinashe K.K
Dept. Electronics and Instrumentation
VJEC, Chemperi, Kerala
from these signals directly in the time domain just by
observing them. EEG signals are highly non-Gaussian, non stationary and non-linear in nature. Hence, important
features can be extracted for the diagnosis of different
diseases using advanced signal processing techniques. The
objective of this paper is to analyze features of human sleep
EEG signals using Power spectral density (PSD) and Fast
Fourier Transform (FFT). These characteristics features can
be used to identify any disorder and thus can play important
roles in diagnosing disorders and pathological conditions.
II.
EEG SLEEP STAGES
In humans, 5 sleep stages and the stage awake are defined
[11], [12]. Each sleep stage is characterized by a specific
pattern of frequency content. The EEG spectrum is divided
in 5 bands
Delta 0 - 4 Hz
Theta 4 - 8 Hz
Alpha 8 - 13 Hz
Beta1 13 - 22 Hz
Beta2 22 – 35 Hz
Stage awake: Signal with alpha activity.
Stage 1: No presence of alpha activity, low beta and theta
activity,
Stage 2: Less than 20 % of delta activity and presence of Kcomplexes and spindles.
K complexes are low frequency waves near 1.0 Hz, with
amplitude of at least 75 mV. Spindles are well defined
waves in the range 11-15 Hz with time duration of more
than 0.5 seconds. There is no criterion about the amplitude
of a spindle.
Stage 3: More than 20 % and less than 50 % of delta
activity,
Stage 4: More than 50 % of delta activity.
Stage REM: Low amplitude waves with little Theta activity
and often saw tooth waves. REM and awake signals might
have a similar shape, but REM has little alpha activity.
In natural conditions a sleep starts by slow wave phase
ranging from shallow sleep stage 1 to deepest stages 3 or 4
and then is replaced suddenly by fast sleep phase. That
forms a single sleep cycle which lasts 90-120 minutes.
During a whole night 4-5 such cycles can be observed for
healthy persons. The duration of fast sleep is minimal at the
sleep onset but gradually increases toward morning. In
contrary, the duration of deep sleep (stages 3 and 4) is
maximal at the 2nd and 3rd sleep cycle and diminishes
toward the sleep end.
III.
METHOD
For automatic sleep stage classification we have used
signals from three channels, EEG, EOG and EMG. All
channels were sampled at 1000 Hz. 30 second epochs were
taken for the analysis. The average EOG power and average
EMG power were used to identify the REM and NREM
stages.
A. Feature selection and Extraction
The power spectral density of the signal, using parametric
methods, is computed as the frequency response of an
autoregressive model of the signal, based on previous values
of the signal. In [8] was found that the order of this model is
very important to obtain an accurate estimation of the
spectrum. The order of the model is selected based on
several criteria. The Akaike's final prediction error (FPE)
criterion was use in [13] and the results show that orders as
low as five can be used to model shorts segments of the
EEG signal; however an order ten is suggested because it
shows better results. The parametric method selected was
the one proposed by Welch; this method always produce a
stable model that minimizes the error on backward and
forward directions and has a good resolution for large
datasets. Figure 1 shows the identified REM stage, slow
wave sleep stage and the Wake stage.
Fig.1.b SWS stage
Fig1.c Wake stage
The fast Fourier transform of the above signals are
obtained as shown in figure2.
Fig2. fft of the selected signals
Fig1.a REM stage
The Welch algorithm is used to identify the stages .The
power spectral density of the EEG signals using the Welch
algorithm is shown in figure 3.
Fig.4.c.Spectrogram of Wake stage
Fig.3.PSD using Welch
The spectrogram of the identified stages of REM sleep, slow
wave sleep and the Wake stage is shown in figure4.
The EOG power of the identified sleep stages were
calculated using the Welch algorithm and is shown in
figure5.
Fig.4. a. Spectrogram of REM sleep
Fig.5.a.EOG power of REM stage
Fig.4.b.Spectrogram of SWS stage
Fig.5.b.EOG power of SWS stage
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IV.
CONCLUSIONS
EEG signal processing is one of the important areas of
research in biomedical signal processing. Medical science
along with the modern engineering techniques can provide
useful information and solution in this field. This paper used
the signal processing techniques of Power spectral density
and Fast Fourier Transform to classify the various stages of
sleep associated with the rat for every 30 s epoch.
Automatic sleep analysis is faster than manual scoring.
Machine processing of 6 hours record takes less than 2
minute but it might take several hours for expert to evaluate
the same record. Automatic analysis is objective because
classification results are not tied with any subjective
experience of human expert. This system can be used in
hospitals for sleep disturbance diagnosis as well as for
fundamental sleep research.
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ACKNOWLEDGMENT
The authors wish to thank NIMHANS Bangalore for the
database and Dr. Laxmi T.Rao, NIMHANS, for the fruitful
discussion s on the scoring techniques.
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