The electroencephalographic (EEG) alterations during the human sleep onset (falling asleep period) has been evaluated by several studies in the past. However, the analysis part has been limited due to standard signal processing methods. This paper has attempted to evaluate a number of advanced parameters for improved sleep onset estimation, such as EEG non-parametric coherence, power frequency and spectral band power. These parameters can be utilised in an on-line algorithm design for neurofeedback applications

Cosic, I

Cvetkovic, D

Dean Cvetkovic

Irena Cosic

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Sleep onset estimator: Evaluation of parameters

RMIT Research Repository

 
 
 
  
Abstract – The electroencephalographic (EEG) alterations 
during the human sleep onset (falling asleep period) has been 
evaluated by several studies in the past. However, the analysis 
part has been limited due to standard signal processing 
methods. This paper has attempted to evaluate a number of 
advanced parameters for improved sleep onset estimation, such 
as EEG non-parametric coherence, power frequency and 
spectral band power. These parameters can be utilised in an 
on-line algorithm design for neurofeedback applications. 
 
I. INTRODUCTION 
 
alling asleep is a complex process. A common sleep 
disorder, insomnia, is often associated with disturbance of 
this complex process. There are electrophysiological, 
cognitive and behavioural changes that occur during this 
transition period. This paper will discuss the 
electrophysiological changes in the electreoencephalogram 
(EEG).  
   What is the moment of sleep onset? How can sleep onset be 
characterised from the sleep electroencephalographic (EEG) 
activity in human adults? These are some of the questions that 
have no clear and definite answer, except that some sleep 
researchers Dement and Kleitman [1] and Rechtschaffen and 
Kales [2] claim that sleep onset in stage 1 is based on criteria 
of alpha reduction. The sleep could be said to begin with the 
arrival of spindles. One report stated that stages 2, 3, 4 and 
rapid eye movement (REM) compose the ‘true sleep’. 
Whereas, the ‘true wake-to-sleep transition’ is measured from 
stages 1 to 2 [3]. The study of the waking and sleeping 
transitions using EEG spectral analysis was mainly indicated 
by the delta and sigma frequencies [4]. Another study 
reported an increased variability in delta, theta and alpha EEG 
bands at the beginning of stage 1 sleep and continued 10 
minutes into sleep [5]. Ogilvie’s study reported theta power 
increase as the alpha and beta decreased during the sleep 
onset period and slow wave sleep [6]. Badia et al., 
investigated the 3 min EEG data sample of transition from 
wakefulness to stage 1 (1.5 min of wakefulness and 1.5 min of 
stage 1), 5 second epochs and single Hz bands [7]. The results 
revealed that the largest decrease in relative spectral power 
was between 9-11Hz at occipital region and the greatest 
increase at 3 and 4Hz, in the frontal and central sites. The 
changes in these bands were described as fluctuating, 
especially in the delta frequency range. 
 
     Manuscript received April 4, 2008.  
Dean Cvetkovic (corresponding author) is with RMIT University, School 
of Electrical and Computer Engineering, GPO Box 2476V, Melbourne, VIC 
3001, Australia, tel: +613 9925 9641, fax: +613 9925 2007, e-mail: 
dean.cvetkovic@rmit.edu.au.  
Irena Cosic is with RMIT University, Science Engineering and 
Technology, GPO Box 2476V, Melbourne, VIC 3001, Australia, tel: +613 
9925 9903, fax: +613 9925 9650, e-mail: irena.cosic@rmit.edu.au.  
   An EEG power frequency variable can be derived from the 
spectral analysis of the EEG signal, such as spectral edge 
frequencies (SEF), for example SEF 95, SEF 90 and SEF 50 
(median frequency). These spectral edge frequencies are 
referred as frequencies below which 95, 90 and 50% of the 
total EEG power is located [8]. For the EEG coherence 
analysis of sleep onset period, the investigation of 
spatiotemporal dynamics is also important. Earlier studies 
reported that delta band coherence between occipital and 
frontal, and central and parietal regions dropped sharply just 
before alpha disappeared. This finding was interpreted as the 
‘hypnagogic’ state which begins just before alpha vanishes 
[9]. Generally, the anterior synchronous activity, quantified 
by coherence, is related to sleep EEG while posterior region 
reflects waking EEG. The focus of this paper has been to 
objectively measure the wake-to-sleep transition, or 
drowsiness period from the sleep EEG activity. We present a 
new method for estimation of wake-sleep (sleep onset) depth, 
consisting of coherence, power frequency and band power 
estimations. 
II. MATERIALS AND METHODS 
A. Subjects 
   One human subject (age: 32, sex: male, weight: 96kg, 
height: 1.76m) was recruited for an overnight 
Polysomnographic (PSG) recording at St. Lukes Hospital 
(Sydney, NSW, Australia). The subject was diagnosed with 
the Apnoea Hypopnoea Index (AHI) of 5.1, representing a 
mild case of hypopnoea.  
 
B. Experimental Protocol 
   The sleep PSG was recorded from 22:30 h until 05:00 the 
next day using Bio-Logic System and Adult Sleepcan Vision 
Analysis (Bio-Logic Corp., USA). The PSG data was 
recorded at 256 Hz sampling rate. Surface electrodes were 
placed on the skull (C3, C4 and O2; 10-20 system) and 
referenced to bridged left and right mastoid to record the EEG 
activity. Two channels were used to record the eye 
movements, with one electrode placed 1 cm above and 
slightly lateral to the outer canthus of one eye and the second 
electrode recording the potentials from an electrode 1 cm 
below and slightly lateral to the outer canthus of the other eye. 
Both electrodes were referenced to left-right mastoid. The 
other electrodes recorded the EMG from the muscle areas on 
and beneath the chin, ECG (using lead-II across the chest 
area), nasal and oral airflow, snoring sounds, breathing effort 
(measured at the chest and abdomen), oxymetry, actigraphy 
recording body positioning and leg movements (right and left 
anterior tibialis).  
   The recorded data was visually scored by the sleep 
technician according to Rechtschaffen and Kales [2] from 30 
Sleep Onset Estimator: Evaluation of Parameters 
Dean Cvetkovic and Irena Cosic, Senior Member 
F
30th Annual International IEEE EMBS Conference
Vancouver, British Columbia, Canada, August 20-24, 2008
978-1-4244-1815-2/08/$25.00 ©2008 IEEE. 3860
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second epochs. The analysis reported 64.5% sleep efficiency 
and  91.8% in sleep maintenance with 132 min spent in wake 
(W) stage, 30 min in stage 1 (S1), 125 min in stage 2 (S2), 17 
min in stage 3 (S3), 49 min in stage 4 (S4), 26 min in stage 
REM, 220 min non-REM and 3 min in movement time. 
III. SIGNAL PROCESSING 
 
   The EEG and EOG data was processed and analysed using 
Matlab software (Mathworks, USA). The EEG-EOG 
correction was applied based on the regression analysis, 
together with source noise removal with the 50 Hz notch 
filter.  The estimation of sleep onset transition was computed 
in EEG signals by applying various coherence, power 
frequency and band power methods. 
A. EEG Inter-Hemispheric Coherence 
   The EEG inter-hemispheric coherence was computed to 
investigate the functional connectivity between the left and 
right brain hemispheres. There was 1 coherence parameter 
computed as a result of 2 EEG recorded channels (C3-C4). 
Coherence estimates the linear cross-correlation between two 
signals as a function of frequency. EEG coherence also 
estimates the degree of synchrony between the electrical 
activities of the two brain regions, concentrating on a certain 
frequency or EEG band. 
( ) ( )( ) ( )fSfS
fS
f
yyxx
xy
xy
2
2
=κ                                                      (1) 
where xy2κ is the estimated coherence range between 0 and 1.  
   In equation (1), S  denotes the spectral estimate of two 
EEG signals x  and y for a given frequency )( f bin. The 
numerator contains the cross-spectrum for x  and y )( xyS , 
while the denominator contains the respective auto-spectra 
for x )( xxS  and y )( yyS . For each frequency )( f  bin, the 
coherence value is obtained by squaring the magnitude 
of ( )fxyκ . The ( )fxy2κ =0 indicates that the activities of the 
signals in this frequency are linearly independent, whereas a 
value of ( )fxy2κ =1 gives the maximum linear correlation for 
this frequency.  
   A well known non-parametric spectral estimation algorithm 
is the Capon’s approach, known as Minimum Variance 
Distortion-less Response (MVDR) [10]. Benesty has 
proposed a method to estimate the magnitude squared 
coherence function [11]. The MVDR spectrum is often 
considered as an output of a bank of filters with each filter 
centered at one of the analysis frequencies. The MVDR 
bandpass filters are both data and frequency dependent in 
comparison to parametric periodogram approach, which is 
both data and frequency independent.  
In the proposed generalized MVDR method, the filter 
coefficients are chosen so as to minimize the variance of the 
filter output, subject to constraint: 
1== k
H
kk
H
k guug                                                                 (2) 
where the unitary matrix is 
U = [ 0u     1u     …   1−Ku ] with 
HH UUUU =  
and K filters of length K are 
 0,[ kk gg =  1,kg   … 
T
Kkg ]1, − , k=0, 1, …, K-1. 
   The magnitude squared coherence function is defined 
between two signals )(nx and )(ny as  
( ) ( )( ) ( )kyykxx
kxy
kxy uSuS
uS
u
2
2
=γ                                                 (3) 
By conducting extended mathematical calculation [10], the 
magnitude squared coherence becomes: 
( ) [ ][ ]kyykHkxxkH
kyyxyxxk
H
kxy uRuuRu
uRRRu
u 11
211
2
−−
−−
=γ                               (4) 
where ( )ku  is a unitary matrix; and ( ) 10 2 ≤≤ kxy uγ . 
B. EEG Frequency Indicator “Brain-Rate”  
   The weighted mean frequency of the EEG spectrum has 
been defined as the ‘brain-rate’ [12] as a preliminary EEG 
frequency indicator of general mental activation (mental 
arousal) and wake-sleep transition. The ‘brain-rate’ (arousal 
indicator) is used to reveal the patterns of sensitivity/rigidity 
of EEG spectrum and is defined as a mean frequency of the 
brain oscillations weighted over all bands of the EEG power 
spectrum. The mental arousal level is based on the quantum 
transition probabilities and is derived [13]: 
f
f
f
f ee
eA 2
2ln
==                    (5) 
where ef is the equilibrium frequency at which the entropy is 
maximal and also corresponds to the dominant frequency 
with eyes closed (6 and 10Hz for children and adults, 
respectively). The functional dependence of arousal )( A  on 
the frequency  )( f  is stated as )( fAA = . 
V
Vff i
i
ib ∑= with ∑=
i
iVV              (6) 
where index )(i denotes the frequency band (for delta )(i =1, 
for theta )(i =2, etc) and iV  is the corresponding mean 
spectrum band potential. The formula for calculation of the 
brain-rate )( bf  is based on the same algorithm used for 
determining the centre of gravity (mass). The brain-rate 
represents the mean EEG frequency weighted over the 
‘whole’ brain potential (power) distribution spectrum.  
C. EEG Frequency Bands 
   The spectral analysis of the sleep EEG signal was 
performed using the short-time Fourier transform (STFT), in 
which the signal is divided into small sequential data frames 
and FFT applied to each one. In STFT analysis, the signal is 
multiplied by a window function ( )tw and the spectrum of 
this signal frame is calculated using the Fourier transform.  
Thus 
( )
2
2)()(, ∫+∞
∞−
−
−= τττ τπ detwxftSTFT fj                  (7) 
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where ( )tx  represents the analysed signal.  
   Prior to performing the STFT on the EEG signal, the 
band-pass filtering was undertaken for extracting absolute 
and relative (band/total) EEG frequency bands, such as: delta 
(0.75-4.5Hz), theta (4.75-7.75Hz), alpha (8-12.25Hz), sigma 
(12.5-15Hz), beta (15.25-24.75Hz) and gamma 
(25-44.75Hz). 
IV. RESULTS 
 
   The scoring of sleep stages was undertaken by visual 
inspection of the sleep technical specialist. After the 
approximately 2 hours of subject laying in bed ‘trying’ to fall 
asleep, the first wake (W) (first 30 epochs) stage was 
identified as an ‘ideal first half of sleep onset’ period of 15 
minutes (1 epoch = 30 seconds). The second part of the sleep 
onset was Stage 1 (S1), from 30-41 epochs (lasting 5.5 
minutes); stage 2 (S2) from 41-70 epochs (14.5 minutes), 
stage 3 (S3) from 70-85 epochs (7.5 minutes); and stage 4 
(S4) from 85-117 epochs (16 minutes). The whole EEG data 
segment was 120 epochs (60 minutes).  
   No statistical analysis was performed on the processed data 
for any significant evidence. Figure 1 shows the analysis 
structure of sleep depth estimation. The results indicated a 
sharp suppression in coherence at 4 Hz (narrow band) from 
around 0.9 to 0.4 from W to S1, followed by the recovery 
period where coherence is fairly consistent around 0.5 Hz 
(Fig. 1a,b). Figure 2 shows the contour representation of the 
coherence at multiple frequencies of 0.7-0.8 magnitude in 
delta band during W stage, 0.6 at 10 Hz (alpha band) and 
0.1-0.4 from 10-25 Hz (alpha-sigma-beta band). However, at 
the actual sleep onset (S1 start), the coherence at both delta, 
alpha and higher EEG bands, disappeared. The coherence of 
0.7 at low delta and of 0.4-0.6 was narrowed to theta-sigma 
band. In the Fig. 1b and Fig. 2, it seems obvious that 
coherence at epoch 20 was equal to 0.  This event at epoch 20 
also had a strong influence on most of the measures (Fig. 
1c,e,f and g). This is an artefact which was caused by the 
movement of the human subject. Similar artefact was also 
present at epoch 55, as shown in Fig. 1 and 2. Therefore, 
further artefact removal is required to process the data and 
improve the accuracy.  
   The ‘brain-rate’ at W was 7-7.3 Hz and during S1 to S2 it 
was decreased to 5.5 Hz (Fig. 1a,c). In the middle of S2, it 
covered back to 7 Hz and progressively decreased to 4 Hz 
during S3 and S4 until it reached the next sleep stage/cycle. 
The EEG relative and absolute band power results revealed a 
clear suppression in alpha band from S1 onset and remained 
fairly constant (absolute) and continuously decreasing 
(relative) during S2-S4, with an exception of irregularity and 
possible movement artefact in the middle of S2 (Fig. 1d,e). In 
terms of relative delta band (Fig. 1g), there is an increase in 
delta relative power from W to S1-4 transition. However, not 
a substantial change was revealed in absolute delta band in W 
to S1 transition (Fig. 1f). There was a noticeable increase in 
absolute delta band from the middle of S2. 
 
  
Fig 1 The analysis structure of sleep depth estimation, characterised by 
transitions between wake (W), stage 1 (S1), stage 2 (S2), stage 3 (S3), stage 4 
(S4) and REM, is expressed by the hypnogram (a). The processed and 
analysed signals include delta 4Hz coherence with its intensity range from 
0-1 (b); the brain-rate power frequency represented in Hz (c); the absolute (d) 
and relative (e) alpha power; and the absolute (f) and relative (g) delta power. 
The shaded area shown in all sub-plots indicate S1 or the sleep onset period.    
 
 
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Fig. 2 The inter-hemispheric MVDR coherence (C3-C4) was computed over the 60 min period (30 sec segments) consisting of W), S1, S2, S3 and S4. The 
legend represents the coherence intensity (0-1). 
 
V. DISCUSSION AND CONCLUSION 
 
   It is possible to objectively estimate the wake-to-sleep 
transition, or drowsiness period from the EEG coherence, 
brain-rate and spectral delta/alpha band parameters [14]. 
There have been many studies, reporting on what is the ‘right’ 
moment of sleep onset [1-6]. We assume that this moment is 
from the onset of stage 1 (S1). All of our parameters 
evaluated have shown a potential as the solid indicators in 
sleep onset detection. However, further testing is needed on 
multiple human subjects, on-line algorithm design in 
neurofeedback applications, artefact removal techniques and 
investigation on EEG spatial and temporal dynamic changes 
during sleep onset [3]. For investigation of these 
spatiotemporal alterations, multiple EEG electrode sites are 
necessary to study the micro-structure.  
ACKNOWLEDGMENT 
 
   The authors gratefully acknowledge Dr. Kwok Yan and Mr. 
Gerard Holland from St. Lukes Hospital (Sleep Complex) in 
Sydney (NSW, Australia), for providing continuous 
consulting, sleep monitoring and scoring input to our sleep 
research.  
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Time (min)
Fr
eq
ue
nc
y 
(H
z)
Coherence (C3 and C4) using MVDR Method
 
 
W S1 S2 30 S3 S4 60
0
4
10
16
22
30
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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Sleep onset estimator: evaluation of parameters

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