Proceedings of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lisbon, Portugal, June 16-18, 2005 (pp10-14)
Wavelet Based CAP Detector with GA Tuning
ROGÉ RIO LARGO1, CRISTIAN MUNTEANU2, AGOSTINHO ROSA2
(1)- Escola Superior de Tecnologia
Instituto Politécnico de Setúbal
Campus do IPS, Estefanilha, 2910-761 Setúbal
PORTUGAL
(2)- LaSEEB-ISR, Instituto Superior Técnico
Universidade Técnica de Lisboa
Av. Rovisco Pais, 1-Torre Norte 6.21, 1049-001 Lisboa
PORTUGAL
http://laseeb.isr.ist.utl.pt
Abstract: - The EEG is an affordable and non-invasive technique to study the brain and very useful in sleep
analysis. The organization of the sleep in stages (global view) was widely used. The sleep EEG microstructure
(local view) gives attention to the short duration EEG events. The CAPS (Cyclic Alternating Pattern
Sequences) is a periodic EEG activity of sleep, and provides important information on EEG synchrony
modulation in the sleep process and is closely related with the dynamic organization of sleep. It is characterized
by repeated spontaneous EEG activations, at intervals up to one minute, from the background activity. The
objective of this work is the automatic detection and classification of CAPS in sleep EEG. We use wavelet
transforms as a tool to analyze the sleep EEG signal in the time-frequency domain, to separate the signal power
in frequency bands. The CAP activation period (A phases) characteristics are used to build a detector and a
state machine determines the CAP sequences periods. A group of sleep registers are tested and results
compared with visual classification. A genetic algorithm is used to make the tuning of the detector.
Key-Words: - EEG, CAPS, Automatic Detection, Wavelets, Genetic Algorithms.
1. Introduction
In sleep analysis, the Electroencephalographic signal
(EEG) plays an important role. This is a non-invasive
technique, and nowadays with the electronic
miniaturization, there is mobile equipment that make
possible to acquire the signals in the patient home.
As a diagnostic tool, the EEG is very affordable
when compared with others techniques that are not so
easy to use and cost a lot more.
The amount of data collected in a normal eight hours
sleep night is very high, and techniques to make their
automatic analysis are of great importance.
The macrostructural organization of sleep is
commonly expressed as a succession of sleep stages
of two types - 4 NREM stages: S1, S2, S3, S4 related
with the sleep depth and a special stage REM
associated with rapid eye movements. They are
classified by standardized criteria [1]. Scoring epochs
of several seconds of signal (20s to 30s) are classified
at a time. The large use of these criteria for long time
has established a good base of experience to
distinguish between normal and pathologic
situations; however this is a static approach to the
sleep process. In recent time, more attention has been
given to the sleep microstructure, which shows that a
more dynamic perspective needs to be adopted [2]. In
effect, the changes in the sleep process are smooth
and gradual, which is not compatible with the abrupt
transitions between the sleep stages in sleep
histograms. The macrostructure analysis could be
seen as a global view of the sleep process. By the
other side the microstructure analysis is a local view.
The sleep EEG microstructure analysis gives
attention to the short duration EEG phenomena
events lasting less than the scoring epoch of sleep
stages. These are commonly designated as arousalrelated phasic events (ARPE) and are generally
associated with a transient lightening of sleep depth.
They appear superimposed on the background
rhythms of sleep EEG and could be seen as a
homeostatic reaction of the organism to make an
adjustment to the environment or internal changes in
order to preserve the sleep. The ARPE could be of
short duration (e.g., vertex sharp waves, isolated Kcomplexes) or long duration such as alpha rhythms
in S1, sequences of K-complexes in S2, delta burst
in S3 and S4 and micro-arousals [3, 4]. The last
group usually is accompanied by increase in muscle
tone and heart rate. A definition of arousals was
proposed by ASDA [5], which basically are sudden
frequency shift toward faster rhythms. Many sleep
Proceedings of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lisbon, Portugal, June 16-18, 2005 (pp10-14)
disturbances are associated with their increase in
number.
The Cyclic Alternating Pattern Sequences (CAPS)
are closely related with the arousals. Both are related
with the dynamic organization of sleep. The CAPS
represents a sustained oscillatory condition between a
greater arousal level (recurring EEG transient or A
phase) and a lesser arousal level (interval between
the successive clusters of EEG transients or B phase)
[6, 7]. In NREM sleep, the A phases are formed by
the arousal-related phasic events characteristic of
each sleep stage, and the B phases by the intervals of
background theta– delta activity. Existence of CAPS
implies a sequence of alternating A and B states with
time duration conditions (2-60 sec) [7]. Situations of
arousal stability coincide with absence of CAP.
Phase A can be divided in 3 subtypes A1, A2, A3 as
a balance between synchronized EEG patterns and
desynchronized patterns preceded or mixed with high
voltage waves [7]. From A1 to A3 there is a
progressive desynchronization.
The objective of this work is the automatic
classification of CAPS in one channel of sleep EEG
signal during all night. This classification has been
mainly made visually [8]. Some preliminary work
was made in the automatic detection [9,10,11]. These
works made comparisons between visual and
automatic classification of CAPS in wealthy subjects
with good results [10], and in patients with less good
results [9]. The present work applies wavelet
transforms to make the filtering of the EEG signal.
This way the features extracted have good time
discrimination. To select the parameters to make the
tuning of the detector we use a genetic algorithm
[12].
2. Material and Methods
2.1. Wavelets to Time-frequency Analysis
The events presented in the introduction can be
described in frequency but have short duration. The
classical Fourier analysis is not well adapted to this
situation. Because the wavelet transform conjugates
good discrimination in frequency and in time, it is
adequate to process the sleep EEG signal in order to
detect and separate those events.
The main goal of this work is the detection and
identification of the CAP activation phase A.
Wavelet transforms are closely related with filter
banks. The idea of multiresolution is basic to wavelet
analysis and divides the frequencies in octave bands:
the compression in time by a factor of two means
expansion in frequency by the same factor.
[ f (t ) →
1 w 

f (2t )] ⇔  F ( w) → F ( )
2 2 

(1)
A fast discrete wavelet transform (DWT) computes
coefficients, applying recursively a pair of half-band
mirror filters (approximation and detail) [13].
Multiresolution analysis decomposes the sleep EEG
signal in a dyadic time-frequency space. Following
the wavelet packet scheme we gain flexibility to
choose the frequency sub bands adequate to
characterise the events.
In the wavelet theory, time-scale is the most natural
one, but as scale and frequency are closely related
we could talk about time-frequency. The relevant
EEG signal activity bands are: delta (0.5-4 Hz); theta
(4-8); alpha (8-12); sigma (12-16); and beta (16-32).
Additionally we separate the delta band in three sub
bands (0.5-1; 1-2; 2-4 Hz). In order to separate them,
we make a wavelet packet analysis to decompose the
sleep EEG signal.
2.2. EEG Features Extraction
For each band the signal power is evaluated. To
discriminate between the background activity and
the phasic activity, two moving average with short
(SMA) and long duration (LMA) are calculated.
The relation between the short (SMA) and the long
moving average (LMA) is used as a measure of the
presence of activity in the EEG bands.
MAr =
SMA ,
LMA
MAr: Moving average ratio. (2)
To make the detection of these activities, the MAr
for each band is compared with thresholds with
hysteresis to mark their beginning and ending.
The characteristics of A phases in CAP periods are:
•Intermittent alpha rhythms and sequences of
vertex sharp waves, in sleep stage 1.
•Sequences of two or more k-complexes with or
without alpha-like components and beta rhythms, in
sleep stage 2.
•Delta bursts showing an increase in amplitude of
at least 1/3 compared to background activities, in
stages 3 and 4
•Transient activation or arousals [5] in all the sleep
stages. [7]
2.3. Detection of Phase A
The beginning of phase A is detected if the activity
index (MAr) of the delta, theta or alpha bands is
higher than the upper level of the threshold.
The ending of phase A is detected if the delta, theta,
alpha and beta bands are all below the lower level of
the threshold (figure 1).
This algorithm detects candidates to be phase A
(pre-A). Their duration and the time interval
between them are tested later to find the CAP
sequences (CAPS).
Proceedings of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lisbon, Portugal, June 16-18, 2005 (pp10-14)
Instead of using alpha band directly, we use an index
between alpha and sigma to separate them better.
Fig.1 Detection of phase A in 60s of EEG.
Aut: Automatic Detection, Vis: Visual Classification.
The power of delta could be used to adjust
dynamically the level of the threshold, and the value
of the hysteresis.
Other parameters are used as indications of the
macrostructure of the sleep: [alpha / (delta+theta)] as
an indication of wake state and [delta / (alpha+beta)]
as indication of slow wave sleep.
population can be mutated, depending on the
probability of mutation.
In ARGA, the mutations are restricted to a
subpopulation of chromosomes, called reservoir.
The reservoir has its individuals mapped onto a fixed
population. The number of chromosomes in the
mutant subpopulation (reservoir) is called diameter
and is adapted during run. If there is no
improvement in the best solution found during a
certain number of generations, the diameter of the
reservoir grows, in order to obtain a larger diversity
in the population and to recast the search in a better
niche of the search space. When this event occurs
(i.e. an improvement beyond a certain threshold) the
diameter of the reservoir is reset to the initial value.
2.6. Comparing Automatic and Visual CAPS
Classification
We used one channel of EEG to make the automatic
classification of the CAPS for each sleep record. Fig.
2 presents the output of one classification automatic
(Caps-Aut) and visual (Caps-Ref). Trace 1 is the
hipnogram (R&K staging [1], 30s epoch)
representing the sequence of sleep stages (Wake,
REM and non-REM: 1, 2, 3, 4). The CAPS is
represented with sequences of A, B phases separated
by intervals of absence of CAPS (non-CAPS).
2.4. CAP Sequences (CAPS)
The time intervals between the phases A are the
phases B.
The duration of the A and B phases must be greater
than 2 seconds and less than 60 seconds. This means
that if two pre-A are not separated by at least 2 s,
they must be put together and if the interval between
them is greater than 60 s, the second pre-A must be
eliminated.
Using these rules and the fact that to start one CAP
sequence, we must have at least 3 consecutive A
phases (2 CAP cycles), and the end of the sequence is
in the beginning of a valid A phase, a state machine,
implement these rules, is used.
The output of the state machine separates the sleep
EEG in periods of CAPS and periods of non-CAPS
(N). Each CAPS interval is marked as a sequence of
phases A and B.
2.5. Genetic Algorithm Tuning
In order to choose the best parameters for the
detector, we use an Adaptive Reservoir Genetic
Algorithm (ARGA), adapted to this situation, where
the exploration/exploitation is directly controlled
during evolution, using a Bayesian decision process
[12]. In a standard GA each chromosome in the
Fig. 2 Automatic and visual CAPS classification for
a sleep record (8h). The top figure is the hipnogram
representing a sequence of sleep stages. The other
represents the CAPS from automatic (middle) and
visual classification (Ref.).
Based on amount of time for each situation, the auto
and visual classifications where compared,
Assuming the visual as a reference. The kind of
errors and agreements that can occur are shown in
table 1. Four global indices are calculated [10]:
• Corr: Correctness. The ratio of all true events (T,
TB and TN) by total events. Global performance of
automatic classification.
• %True: It is the ratio of correct A phase (T) by
total visual A phase in (T+M+MA) - first column.
Proceedings of the 6th WSEAS Int. Conf. on EVOLUTIONARY COMPUTING, Lisbon, Portugal, June 16-18, 2005 (pp10-14)
• False/Ref.: It is the ratio of false A phases (F+FA)
– first line, by total visual A phase in (T+M+MA) first column.
Table 1. Auto-Visual CAPS Classification
Visual
Visual
Visual
Comparisons
A
B
NCAP
T
F
FA
Auto A
true A
false
false A
M
TB
FB
Auto B
miss
true B
false B
MA
MB
TN
Auto NCAP
miss A
miss B
True N
3. Results
A group of parameters from the detector is tuned
with the genetic algorithm (ARGA). Fig. 3 shows the
dynamic of the ARGA after 20 generation.
A line connecting the top points could be interpreted
as a ROC. The value marked as the best point was
selected by the GA as the best tuning for this
example.
The comparison results for 6 patients are shown in
table 2. Miss=1-True.
In present work the results obtained with the group
of patients are concentrated around the mean values
and similar with the human inter-rate scorer [9].
Table 2. Results for 6 patients
%True
%Miss
Subjects
68
Pat. 1
32
67
Pat. 2
33
63
Pat. 3
37
61
Pat. 4
39
75
Pat. 5
25
70
Pat. 6
30
Mean
67
33
False/Ref.
43
65
25
30
62
36
44
4. Conclusion
Fig. 3 Dynamic of ARGA for 20 generation.
The total output obtained in the tuning process is
shown in fig.4. It represents the %True versus the
ratio of False/Ref. Each point is a run of the GA with
one set of parameters.
Fig.4 – Results of the tuning with ARGA. The %True
is represented in function of the False/Ref. The line
represents a curve similar to a ROC.
CAP studies by visual analysis are a heavy task. The
spreading of this paradigm in sleep study will need
the existence of reliable automatic detection
systems. This work presents an approach to this task
using wavelets to extract features from the sleep
EEG signal. The wavelets have a good timefrequency characteristic allowing good time
discrimination. The present study used time
discrimination of 0.25s. The agreement between
automatic and visual score is comparable with the
agreement between different human scorers [9].
Some improvements are being prepared in order to
have better detection and to include the separation of
the different phases A.
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