Signal regularity-based automated seizure detection system for scalp EEG monitoring
Розглянуто роботу автоматизованої системи реєстрації ЕЕГ головного мозку для раннього виявлення епілептичних нападів. Розроблено комп’ютерний алгоритм для перетворення складних багатоканальних сигналів ЕЕГ мозку на кілька динамічних показників, супроводжуваних дослідженнями їхніх просторово-часових...
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| Published in: | Кибернетика и системный анализ |
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| Date: | 2010 |
| Main Authors: | , , , , , , , , |
| Format: | Article |
| Language: | Russian |
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Інститут кібернетики ім. В.М. Глушкова НАН України
2010
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| Online Access: | https://nasplib.isofts.kiev.ua/handle/123456789/45648 |
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| Journal Title: | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| Cite this: | Signal regularity-based automated seizure detection system for scalp EEG monitoring / Deng-Shan Shiau, J.J. Halford, K.M. Kelly, R.T. Kern, M. Inman, Jui-Hong Chien, P.M. Pardalos, M.C.K. Yang, J.Ch. Sackellares // Кибернетика и системный анализ. — 2010. — № 6. — С. 74–88. — Бібліогр.: 21 назв. — рос. |
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Digital Library of Periodicals of National Academy of Sciences of Ukraine| _version_ | 1859728588998180864 |
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| author | Deng-Shan Shiau Halford, J.J. Kelly, K.M. Kern, R.T. Inman, M. Jui-Hong Chien Pardalos, P.M. Yang, M.C.K. Sackellares, J.Ch. |
| author_facet | Deng-Shan Shiau Halford, J.J. Kelly, K.M. Kern, R.T. Inman, M. Jui-Hong Chien Pardalos, P.M. Yang, M.C.K. Sackellares, J.Ch. |
| citation_txt | Signal regularity-based automated seizure detection system for scalp EEG monitoring / Deng-Shan Shiau, J.J. Halford, K.M. Kelly, R.T. Kern, M. Inman, Jui-Hong Chien, P.M. Pardalos, M.C.K. Yang, J.Ch. Sackellares // Кибернетика и системный анализ. — 2010. — № 6. — С. 74–88. — Бібліогр.: 21 назв. — рос. |
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| container_title | Кибернетика и системный анализ |
| description | Розглянуто роботу автоматизованої системи реєстрації ЕЕГ головного мозку для раннього виявлення епілептичних нападів. Розроблено комп’ютерний алгоритм для перетворення складних багатоканальних сигналів ЕЕГ мозку на кілька динамічних показників, супроводжуваних дослідженнями їхніх просторово-часових властивостей. Робота алгоритму аналізується на великому клінічному наборі даних.
The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multi-channel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset.
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UDC 519.6
DENG-SHAN SHIAU, J.J. HALFORD, K.M. KELLY, R.T. KERN, M. INMAN, JUI-HONG CHIEN,
P.M. PARDALOS, M.C.K. YANG, J.CH. SACKELLARES
SIGNAL REGULARITY-BASED AUTOMATED SEIZURE DETECTION
SYSTEM FOR SCALP EEG MONITORING
1
Keywords: seizure detection, scalp EEG, pattern match regularity statistic (PMRS),
local maximum frequency, amplitude variation, artifact rejection, sensitivity, false
detection rate.
1. INTRODUCTION
Seizures result from the sustained, organized, and synchronized discharge of massive
numbers of cerebral neurons. Seizures may begin locally in one cerebral hemisphere
(where they may remain, or may spread to involve the ipsilateral or both hemispheres
diffusely), or they may originate in both cerebral hemispheres simultaneously.
Electroencephalography (EEG) is the recording of electrical activity along the scalp
produced primarily by summed synaptic potentials of neurons within the cortex of the
brain. Long-term EEG monitoring has become a standard procedure for seizure
diagnosis and classification, and for presurgical evaluation of patients with medically
refractory seizures. In the United States, the mean length of stay for patients with
intractable epilepsy admitted to an epilepsy monitoring unit (EMU) for diagnosis
and/or pre-surgical evaluation ranges from 4.7 to 5.8 days with total aggregate
charges exceeding $200 million per year [1].
Long-term video-EEG monitoring is usually performed in an EMU. Typically,
seizures recorded in EMUs are identified by direct observation of the medical staff or by
patients pressing an alarm. However, observers are sometimes not present and/or patients
are often unaware of their seizures [2]. In general, unnoticed seizures can be detected by
post hoc review of the continuously recorded video-EEG data. This time-consuming
review has to be performed at least by trained EEG technologists in order to achieve
acceptable accuracy and sensitivity. However, in most centers, complete review of the
entire recording by an electroencephalographer for each patient (mostly 4–5 days) is
almost impossible due to the high volume of patients and a limited number of qualified
EEG experts. As a result, recorded seizures, particularly non-convulsive seizures, can
escape detection, potentially compromising patient evaluation and/or unnecessarily
prolonging the patient’s hospital stay. Therefore, since the 1980s, attempts have been
made to develop computerized algorithms for automated seizure detection systems for
scalp (non-invasive) EEG recordings [3–10]. Such a system with reliable detection
performance would ensure rapid detection and timely review of seizures, thereby
minimizing the time required for diagnosis and reducing manpower requirements for data
review and analysis. The use of automated tools could decrease the lengths of stay and
result in reduced patient risk, lowered cost per patient, and better utilization of limited
specialty resources. However, to date, the high false-positive rate of commercially
available automated seizure detectors renders them of limited use.
It has long been recognized that designing a computer algorithm to detect seizures
from scalp EEG is much more challenging than that from intracranial/depth recordings
(see [11–14] for intracranial EEG seizure detection). Scalp EEGs are more sensitive to
the recording environment (e.g., electrode failure, electrical artifacts), muscle and
74 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
1This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS) from NIH-NINDS.
© Deng-Shan Shiau, J.J. Halford, K.M. Kelly, R.T. Kern, M. Inman, Jui-Hong Chien, P.M. Pardalos,
M.C.K. Yang, J.Ch. Sackellares, 2010
movement artifacts (e.g., chewing), as well as to the normal physiological state changes
(e.g., sleep-awake cycle). The EEG patterns of these artifacts and activities share a
certain degree of signal characteristics similar to those of ictal EEGs, such as signal
amplitude and frequency, and thus could generate numerous false detections. Therefore,
a successful scalp EEG seizure detection algorithm must include a robust artifact
rejection module that is able to distinguish, spatially or temporally, the signal
characteristics between artifact and true ictal EEG epochs.
In this report, we introduce a novel automated seizure detection algorithm for
accurate and rapid analysis of long-tem scalp EEG recordings to identify ictal EEG
segments. The algorithm reads, analyzes, and outputs a binary response (seizure or not
seizure) for each sequential 5.12 seconds. For each computation epoch, the analysis
involves translating each of the EEG channels into six EEG descriptors: 1) pattern-match
regularity statistic (PMRS); 2) local maximal frequency (LFmax); 3) amplitude variation
(AV); 4) local minimal; 5) maximal amplitude variation (LAVmin and LAVmax); and
6) maximal amplitude in a high frequency band (AHFmax). The spatiotemporal patterns
of these descriptors are used for detecting ictal EEG epochs as well as rejecting the EEGs
with significant artifacts. PMRS is used as the primary detector, whereas the other
descriptors are used for artifact rejection. The algorithm generates over 800 descriptor
traces (both discrete/binary and continuous variables) across 16 EEG channels. The
parameters of the algorithm were trained and optimized based on the detection
performance, in terms of detection sensitivity and false detection rate, in 47 long-term
scalp EEG recordings (47 subjects, 141 seizures in a total of 3652.5 hours of recording).
Its detection performance was then validated in a separate test EEG dataset (55 subjects,
146 seizures in a total of 1208 hours of recording). Using the same test dataset, the
performance of the proposed algorithm was compared with commercially-available seizure
detection software, Reveal® (Persyst Development Corporation).
Detection delay, defined as the time elapsed between the occurrence of the first clear
changes in an electrographic seizure pattern and the first epoch detected by the algorithm,
was not included in the performance assessment in this study. For a seizure detection
algorithm that is coupled with an automated intervention system aimed at controlling
and/or aborting a seizure, it is critical to detect a seizure event within seconds of the
electrographic onset. Usually such a detection algorithm is applied to intracranial EEG
recordings [15–17]. A scalp EEG-based seizure detection algorithm is usually used for
fast off-line long-term EEG review or for on-line detection to alert nursing staff when
a seizure occurs. From a validation point of view, the detection delay for these applications
is less important than the ability to detect the seizure’s occurrence — electrographic seizure
onset times are sometimes indeterminate, even among experienced epileptologists.
The remainder of this paper is organized as follows: Section 2 gives detailed
descriptions regarding data characteristics (2.1), EEG descriptor calculations (2.2),
detection algorithm design (2.3), and performance assessment and statistical
analysis (2.4). Section 3 presents the results of detection performance, its assessment, and
statistical comparisons. Section 4 presents the conclusions and discussion.
2. METHODS
2.1. Data Characteristics
2.1.1. Test Subjects. All patients were 18 years of age or older with a history of
suspected or intractable seizures admitted to Allegheny General Hospital (AGH,
Pittsburgh, PA) or the Medical University of South Carolina (MUSC, Charleston, SC) for
long-term EEG-video recordings for diagnostic or pre-surgical evaluation, respectively.
Collection and analysis of their EEG recordings was approved by AGH’s and MUSC’s
Investigational Review Boards, as well as by the Western Investigational Review Boards
(WIRB). All patients whose consents were received were included in the study. There
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were no exclusion criteria. The participated patients included a statistically equal number
of males and females, and an ethnic and racial distribution that reflected the regional
population of each clinical site.
2.1.2 . Datasets (EEG).
Technical Information — EEG
recordings collected from AGH
were obtained using 128 channel
Nicolet BMSI-6000 long term
monitoring systems (Viasys,
Madison, WI, USA) with a 400 Hz
sampling rate, whereas data
collected from MUSC used XLTEK
EEG monitoring systems (Oakville,
Ontario, Canada) with a 256 Hz
sampling rate; both institutions used
a montage that included a 19-elect-
rode international 10–20 system
(Fig. 1). Only 16 channels (exclu-
ding Fz, Cz, and Pz) from a refe-
rential montage were utilized in the
proposed seizure detection algo-
rithm. The referential channel was
decided at the clinical sites, but was
usually placed at a location between Cz and Pz, as recommended by the American
Clinical Neurophysiology Society [15]. In order to reduce the effects from muscle and
movement artifacts, each of the 16 EEG signals were band-pass filtered with
a low cut = 1 Hz and high cut = 20 Hz, which covers the frequency ranges of most of the
ictal EEG patterns classified in [16].
Training Dataset — this detection algorithm was developed, trained, and optimized
in 47 long-term scalp EEG recordings, which contained a total of 141 epileptic seizures
in 3652 hours. In order to develop as robust an algorithm as possible, the entire recording
from each subject was included in the analysis. As a result, the algorithm was trained
with a wide range of seizure patterns, physiologic states, and a variety of artifacts from
different resources that are commonly encountered in the clinical setting. Because this
dataset was only used for developing and training purposes, identification of the seizure
events were primarily based on the clinical seizure reports provided by the collaborative
clinical sites (AGHI and MUSC). However, to reduce the possible false positives and
negatives from the clinical reports, all the EEG recordings were further reviewed in-house
by the algorithm development team. When seizure events were identified in-house but were
not noted in the clinical reports (or vice versa), the discrepancies were verified with the
investigators at the clinical sites where both EEG and video were available.
Validation Dataset — the data for performance validation consisted of 436 EEG
segments, 2 ~ 3 hours each, sampled from long-term EEG recordings obtained from a
separate group of 55 subjects. The total length of the test EEG segments was 1208 hours.
Because this dataset was for algorithm validation, it was important that seizure events
were determined by an independent review panel that was not involved with the
algorithm development. Therefore, all of the EEG segments were independently
reviewed by three EEG experts (epileptologists) and a majority rule (i.e., at least 2 out
of 3) was applied to determine the occurrences of seizure events in the dataset. As
a result, the review panel identified a total of 209 electrographic seizures. However,
53 seizures were identified in one patient, and 7 other subjects had more than 5 seizures
identified. To avoid the overall detection performance being overly-contributed from any
76 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
Fig. 1. The international 10–20 system seen from above the
head: C = central, T = temporal, P = parietal, F = frontal,
Fp = frontal polar, and O = occipital
one patient, seizure events were randomly down-sampled to 5 seizures in all patients with
more than 5 reviewer-identified seizures. As a result, a total of 146 electrographic
seizures were included in the validation study.
2.2. EEG Descriptors
The seizure detection algorithm calculates six primary EEG descriptors over time
(sequential non-overlapping 5.12 s) for each of the 16 EEG signals: pattern-match
regularity statistic (PMRS), local maximal frequency (LFmax), amplitude variation
(AV), local minimal and maximal amplitude variation (LAVmin and LAVmax), and
maximal amplitude in a high frequency band (AHFmax). Detailed description for
each descriptor is given below.
2.2.1. PMRS. For a given time series data, it is important to know how
regular/complex it is. Motivated by the algorithm for calculating approximate entropy
(ApEn) [17], we developed a new statistic, PMRS, which quantifies the regularity of
a given signal. As with ApEn, it can be used to distinguish normal from abnormal data in
instances where moment statistics (e.g., mean and variance) approaches fail to show
meaningful differences. Compared to many nonlinear dynamic statistics, a major
advantage of PMRS is its ability to interpret both stochastic and deterministic models.
The calculation of ApEn is based on a conditional probability of the next
corresponding points being value-matched given that the previous m corresponding
points are all value-matched, for a fixed integer m. That is, for any subsequence xi of
length m in a given time series U, estimate:
Pr{difference of the next points of xi and x rj � | xi and x j and value matched},
where value match is defined as the maximum difference between the corresponding
points of two subsequences being less than a critical value r. One main limitation of
ApEn is that for a given time series with no further information, it is almost impossible to
know how to choose its parameters m and r. For different selections of parameters m and r,
ApEn could yield very inconsistent results, even when the choices are within a
reasonable range. One reason for this inconsistency could be due to the value match
criterion being very sensitive to the critical value r and hence the degree of difficulty to
satisfy the value match criterion increases fast even with a small increase of m . Besides,
even when two subsequences are value matched to each other, they may have very
different patterns, which could be considered a meaningless match in practice. For
example, when the noise portion in a time series has much smaller variation than the
other meaningful portion, the value match criterion may have more matches from the
subsequences in the noise part. For these reasons, we proposed to use the criterion of
pattern match instead of value match to evaluate the regularity of a time series. The
following defines the criterion of pattern match.
Suppose that U u u un� { }1 2, ,... , is the time series to be investigated, and let �� u be
the sample standard deviation ofU. For a fixed integer m , define a series of subsequences
of U such that x u u u i n mi i i i m� � � � �� � �{ }, , ... , ,1 1 1 1. Then for a given positive real
number r (e.g., r u� 02. �� ), xi and x j are considered pattern match to each other if:
1. | | , | | ,u u r u u ri j i m j m� � � �� � � �1 1 and
2. for k m� �12 1, ,... , , sign sign( ( )u u u ui k i k j k j k� � � � � �� � �1 1 .
The first condition of this criterion means that the beginning points and the end
points of two subsequences must be valued matched, i.e., two subsequences have to be
approximately at the same range. The second condition indicates that the points in
between must have the same pattern, i.e., pattern matched to each other. Obviously, this
matching scheme decreases the degree of dependence on the parameters m and r because the
exact value match for the corresponding points of the two subsequences is not required. For
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6 77
the purpose of demonstration, Fig. 2
shows an example of the two subsequen-
ces xi and x j that have very similar
structure to each other (i.e., pattern
match). However, when considering the
values for each of the corresponding
points, they may not be accepted by the
value match criterion depending on how
large the critical value r is. Next, we
describe how to calculate PMRS using the
pattern match criterion.
The calculation of PMRS is based on
the estimation of the conditional
probability that the next points of xi and
x j have the same change of sign, i.e., sign (ui m� � u u ui m j m j m� � � � �� �1 1) ( )sign ,
given that xi and x j are pattern-matched to each other. That is, for each subsequence xi
of length m , define
p u u u u x patti i m i m j m j m j� � � �� � � � � �Pr {sign sign is( ) ( ) |1 1 ern match xiwith },
where xj is any subsequence of length m in U. Then, by given a time series U of n points,
for 1� � �i n m , we can estimate pi by using the sequences x x xn m1 2, , ... , � in U as
�
# ’ ( )
p
x s x u u
i
j i i m i m
�
� �� � �of pattern match with and sign 1 sign
pattern match with
( )
# ’
u u
of x s x
j m j m
j i
� � �� 1
,
and PMRS is calculated as �
�
�
�
�
1
1n m
pi
i
n m
ln ( � ) . Intuitively, when the time series U is
more regular, �pi ’s should be larger and therefore PMRS will be smaller.
Simulation Study of PMRS: To demonstrate the utility of PMRS and its
comparison with ApEn, we used a MIX( )p process to illustrate the regularity in
correlated stochastic processes with a continuous state space (Pincus, 1991). The
construction of MIX( )p process is as follows:
i. Define a jj � 2 2 12sin( / )� , for all j � 12, ,... .
ii. Let bj be a family of independent identically distributed (i.i.d.) real random
variables with uniform density on the interval [ , ]� 3 3 .
iii. Let c
j
be a family of i.i.d. Bernoulli random variables: c j � 1 with probability
p and c j � 0 with probability 1� p, 0 1� �p .
iv. Define MIX( ) ( )p c a c bj j j j j� � �1 .
This is a family of stochastic processes that samples a sine wave when p � 0 and
samples an i.i.d. uniform random variable when p � 1. Figure 3 shows 200 points of the
simulated MIX( )p processes when p = 0, 0.25, 0.50, 0.75, and 1. Obviously, the MIX( )p
process becomes more random as p increases. Furthermore, the MIX( )p process has
mean 0 and variance 1 for all p, which means that the first and second moment statistics
cannot be used to distinguish the degree of regularity (randomness) for different values of p.
78 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
Fig. 2. An example of good pattern match but not
value match
j-sequence
i-sequence
There are two purposes of this comparison. The first purpose is to compare the
sensitivity with respect to the change of the randomness parameter p. Theoretically, both
PMRS and ApEn should increase when p becomes larger. The second purpose is to compare
the consistency of the measure with respect to the different values of parameters m and r in
the algorithms. To accomplish these two purposes, PMRS and ApEn profiles ( p = 0, 0.01,
0.02, …, 0.99, 1) were generated for m= 2, 3, 4, and 5; r = 0.14, 0.16, 0.18, 0.20, and 0.22.
Because the curves may have different scales and we are only interested in how the measures
change over p, for the purpose of comparison, the ranges of all curves are normalized from 0
to 1. Each value in the curves is based on an average of 10 values from a Monte Carlo
simulation, and the sample size for each simulation is 1000 points.
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6 79
Fig. 3. MIX( )p process for different degree of regularity
0 20 40 60 80 100 120 140 160 180 200
2
1
0
–1
–2
2
1
0
–1
–2
2
1
0
–1
–2
2
1
0
–1
–2
2
1
0
–1
–2
p � 0 75.
p � 1
p � 0 50.
p � 0 25.
p � 0
Fig. 4. Normalized ApEn and PMRS versus p in the MIX( )p process for different values of m, given r = 0.18
P
M
R
S
p p
A
p
E
n
m � 2
m � 3
m � 4
m � 5
m � 2
m � 3
m � 4
m � 5
Figure 4 shows the curves of ApEn and PMRS values over p, where the dimension
of the constructed subsequences m = 2, 3, 4, and 5, all using a filtering critical value
r = 0.18 as used by Pincus (1991). It is clear that ApEn gives inconsistent results with
different values of m . Although PMRS could only distinguish well between p = 0 and
0.5, the results are robust across values of m.
Similarly, Fig. 5 demonstrates comparisons between ApEn and PMRS over different
values of r, given that m = 3. Again, ApEn shows inconsistent results for different values
of r, whereas the results of PMRS are much more consistent. This is because the pattern
match criteria are less dependent on the critical value r. Besides, in this example, PMRS
distinguishes the degree of regularity better than ApEn.
Demonstration of PMRS on EEG Analysis and Ictal Activities. One of the most
recognizable signal characteristics of the EEG during a seizure is its highly regular and
organized patterns. This pattern usually consists of multiple EEG channels each with
a period (seconds to minutes) of continuous repeated waveforms, typically between 2 to
20 Hz in scalp EEG depending on the type of ictal discharge [16]. Because PMRS can be
used to detect signals with high regularity, it was used as the primary seizure detector in
this seizure detection algorithm. Figure 6 demonstrates the behavior of PMRS values from
one EEG channel (T3, the trace in red) that was involved in an ictal discharge. It is clear
that, due to the more rhythmic signals during the seizure period, PMRS values drop
significantly when compared with the periods before and after the seizure. Therefore, with
a proper threshold, the seizure can be easily detected with the change of PMRS values.
2.2.2. EEG Descriptors for Artifact Rejection. Although PMRS can be very
sensitive in detecting seizure activities, there are many other EEG patterns with high
regularity. For example, the signals dominated by muscle activity are also very regular
but with a much higher frequency compared to ictal EEG patterns. Certain sleep EEG
patterns can also be very rhythmic and organized, but they usually involve certain EEG
channels such as O1 and O2 (occipital region) and have almost no muscle activity.
Signals dominated by recording artifacts (due to machine or electrode failure) can be
regular but often with much larger amplitudes. To reject these unwanted segments with
possible changes in PMRS values, several amplitude- and frequency-based EEG
descriptors were incorporated in the detection algorithm.
Local maximal frequency (LFmax) observes the maximal frequency of
one-directional (negative to positive) zero crossings among the 11 overlapping 1-second
time windows within a detection window (5.12 seconds in the algorithm). EEG signals
were normalized to zero mean before searching for crossings. Two consecutive 1-second
windows are overlapping for 600 ms. LFmax can be used to detect an EEG segment that
is dominated by high frequency muscle activities (Fig. 7). LFmax was calculated for each
of the 16 EEG channels used in the detection algorithm.
80 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
Fig. 5. Normalized ApEn and PMRS versus p in the MIX( )p process for different values of r, given m � 3
P
M
R
S
p p
A
p
E
n r � 0 14.
r � 0 16.
r � 0 18.
r � 0 20.
r � 0 22.� � �
r � 0 14.
r � 016.
r � 0 18.
r � 0 20.
r � 0 22.� � �
Amplitude variation (AV) is simply the standard deviation of the EEG amplitudes
within a detection window. In the absence of seizure (ictal) patterns, high AV can
indicate the patient is eating (chewing artifact, an example shown in Fig. 8a) or
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Fig. 6. An example of a PMRS curve before, during, and after a seizure (between the two vertical dotted red
lines). PMRS values drop significantly during the ictal period compared to the other periods
P
M
R
S
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0 2 4 6 8 10 12 14 16
1 sec
50 �v
Minutes
Seizure
Fig. 7. An example of an LFmax curve before, during, and after a muscle activity (between the two vertical
dotted red lines). LFmax values are typically larger than 15 during muscle activities, and are significantly
larger than other periods.
1 sec
50 �v
Minutes
Muscle Activity
0 2 4 6 8 10 12 14 16
24
22
20
18
16
14
12
10
8
6
4
a recording artifact (Fig. 8b) is present. Following the onset of seizure (ictal) patterns,
high AV can indicate the patient has progressed to a more generalized (i.e., tonic-clonic)
event with a large amount of muscle activity. Besides, a minimal AV threshold is also
used as a necessary condition for detecting a seizure activity. As with LFmax, AV was
calculated for each of the 16 EEG channels used in the detection algorithm.
Local minimal and maximal amplitude variation (LAVmin and LAVmax) calculate
minimum and maximum local non-overlapping 1-second amplitude variation (standard
deviation) within a detection window, respectively. They are used to capture small local
changes in signal amplitudes (e.g., eye movement or brief muscle activities) that could be
missed in overall amplitude variation. LAVmax and LAVmin were calculated for each of
the 16 EEG channels used in the detection algorithm.
Maximal amplitude in a higher frequency band (AHFmax) observes the maximal
amplitude of the [25~70] band-passed filtered EEG signal within each detection window.
AHFmax was used to detect the existence of any muscle activities, which is a necessary
condition for an ictal EEG segment in most of the ictal EEG patterns. AHFmax was only
calculated for 8 of the 16 EEG channels used in the detection algorithm: O1, O2, F7, F8,
T3, T4, T5, and T6.
2.3. Seizure Detection Algorithm Design
2.3.1. Overview. The detection algorithm analyzes previously recorded scalp EEG
records sequentially for each non-overlapping 5.12 second EEG segment and reports a
seizure detection when sufficient criteria are met. The choice of the window length was
empirical, based on the consideration of accuracy of the EEG descriptor estimates as well
as of the stationarity of the signal within the window. EEG signals are band-pass filtered
before the EEG descriptor calculations. Once all of the necessary EEG descriptors are
calculated, the algorithm analyzes their spatiotemporal dynamics to determine if an EEG
segment passes a set of artifact rejection criteria (ARC) designed to reduce false
82 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
Fig. 8. (a) An example of AV curve before, during, and after a chewing activity (between the two vertical
dotted red lines). AV values during a chewing activity are significantly larger than other periods. (b) An
example of AV curve before, during, and after a recording artifact (between the two vertical dotted red lines).
AV values during a recording artifact are significantly larger than other periods
0 2 4 6 8 10 12 14 16
Minutes Minutes
AV AV
1 sec
50 �v
a b
Recording
Artifact
Chewing Activity
100
90
80
70
60
50
40
30
20
10
120
100
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60
40
20
detections, as mentioned previously. When passed, the EEG segment is further examined
to determine if its signal characteristics match with the criteria designed for detection of
unilateral onset (left- or right-sided) or bilateral onset seizure activity. The criteria consist
of comparisons of the aforementioned mathematical descriptors against a series of
thresholds, using different channel combinations. If all signal characteristics pass the
criteria, then an event detection is reported automatically by storing the detection time.
EEG-trained users can easily review the stored times and associated EEG segments to
confirm or deny the presence of true seizure activity.
2.3.2. Detailed Algorithm Description. The flow chart depicted in Fig. 9 illustrates
the algorithm for seizure detection. This section details each of the 9 steps in the process.
Step 1. The algorithm imports one 5.12 epoch of 16 referential EEG channels, as
depicted in block 1.
Step 2. Imported EEG signals are band-pass filtered, as illustrated in block 2, with
two frequency band: A � [ , ]120 and B � [ , ]25 70 . The A-filtered signals are used to
calculate PMRS, LFmax, AV, LAVmin, and LAVmax, while the B-filtered signals are
used only to observe the descriptor AHFmax.
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6 83
Fig. 9. A flow chart of the seizure detection system
Import one calculation epoch (5.12s) of
EEG signals noin recorded data file
Generate filtered fEEG in two
desired frequency ranges A&B
Calculate and store EEC descriptors PMRS,
LFmnx, AV, LAVmin, and LAVmax using
fIEEG(A), and AHFmax using fEEG(B)
Calculate and store baseline Mean
X I( ) and Standard Deviation S I( )
of PMRS among previous segments
Save the time of the Seizure
detection
End — Detected seizure times
marked for review mid possible
deletion by EEG trained user
Pass criteria for
Left-unilateral Seizure
detection
Or
Pass criteria for Right-unilateral
Seizure detection
Or
Pass criteria for bilateral
Seizure detection
End of
Recording?
Pass ARS
Yes
Yes
Yes
No
No
No
1
2
3
4
5
6
7
8
9
Step 3. After generating filtered EEG signals for each EEG channel signal, the
algorithm calculates all necessary EEG descriptors, as illustrated in block 3.
Step 4. Based on EEG descriptors calculated in step 3, a set of artifact rejection
criteria (ARC) is applied as shown in block 4 to reject unwanted EEG segments that
contain:
(1) Any recording artifact that causes “over-synchrony” among EEG channels. This
type of artifact is detected (and rejected) if the algorithm observes the percentage of channel
pairs that exhibit similar PMRS or AV values (i.e., difference � �) is high (e.g., > 50%).
(2) Any sleep EEG pattern that causes low amplitude and high regularity in many
EEG channels. This type of artifact is detected (and rejected) if the algorithm observes at
least 7 channels exhibit low PMRS values and low LAVmin.
(3) Any activity that causes EEG signals containing almost no muscle activity,
which is not normal during a typical ictal activity. This type of artifact is detected (and
rejected) if the algorithm observes low AHFmax values in many EEG channels over
temporal and occipital regions (i.e., O1, O2, F7, F8, T3, T4, T5, and T6).
(4) Any sleep EEG activity that causes significant difference of signal regularity
between the occipital/posterior-temporal (O1, O2, T5, and T6) and temporal/frontal-
temporal (T3, T4, F7, and F8) brain regions. This type of artifact is detected (and
rejected) if the algorithm observes the difference of the mean PMRS values between the
two groups is large.
Step 5. Once an EEG epoch passes the ARC, the algorithm calculates the sample
mean and standard deviation of the PMRS values over the preceding 60 epochs
(~ 5 minutes interval), shown in block 5, to establish a baseline threshold for the
detection of a significant drop of a PMRS value. This is based on the observation that
PMRS values for certain EEG channels decrease significantly at the seizure onset (as
shown in Fig. 6). To reduce the effects from signal artifacts to the baseline values, the
PMRS values are automatically adjusted to a preset “interictal” value (= 0.6) if the
corresponding LFmax or AV values exceed preset thresholds.
Step 6. After determining the PMRS detection threshold, the algorithm compares
the current EEG descriptors with three sets of criteria for detecting a seizure event, as
illustrated by block 6. These three sets of criteria were designed based on the EEG signal
characteristics of (1) left-unilateral EEG seizure onset, (2) right-unilateral EEG seizure
onset, and (3) bilateral EEG seizure onset.
Step 7. After a seizure is detected, as illustrated by block 7, the system will save the
detection time. This allows the software to call the user’s attention to that segment for
possible seizure activity.
Step 8. After the detection process is finished for the current epoch, the system
checks if the end of the recording was reached, as depicted by block 8.
Step 9. If the end of the recording was reached, the algorithm will stop processing,
as illustrated by block 9. The algorithm then allows the user to review the entire EEG
record, calling attention to the seizure detection times stored in step 7, for possible
seizure activities. If the end of the record was not reached, the algorithm goes back to
step 1 and reads and analyzes the next 5.12-second EEG epoch.
2.4. Performance Assessment and Statistical Analysis
2.4.1. Determining True and False Detections. A detection reported by the
algorithm is considered a true detection when the time of the detection was within two
minutes of the seizure onset (determined by the EEG reviewers), and is considered a false
detection when the detection time was outside the two minute interval of any
electrographic event identified by the review panel. The two-minute detection window
was chosen based on a general observation that most seizures have ictal period between
30 seconds and 2 minutes. Furthermore, as discussed previously, the purpose of the
detection algorithm described and tested in this study is to identify electrographic seizure
84 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
events in pre-recorded scalp EEGs. Therefore, we consider a two-minute detection window
appropriate for the performance evaluation in this study.
2.4.2. Estimating Performance Statistics — Detection Sensitivity and False
Detection Rate.
Detection Sensitivity. Because each subject had a small number of seizures (� 5, as
determined by the EEG review panel or down-sampled as needed), it would not be
meaningful to calculate individual detection sensitivity. Therefore, we calculated an
overall detection sensitivity using the total number of true detections divided by the total
number of electrographic seizures (146 seizures in 55 test subjects). This gives a better
estimation of the true sensitivity of a test algorithm. The 95% confidence interval of the
sensitivity estimation was constructed using the bias-corrected and accelerated (BCa)
bootstrap confidence interval [18]. The bootstrap analysis was performed using the
statistical software Splus/R.
False Detection Rate. Because this study included sufficient lengths of EEG
recordings (range 14.25 ~ 30.521 hours; mean = 21.97 hours) with different physiologic
states in each test subject, the false detection rate, calculated as the number of false
detections divided by the total number of EEG hours, for each test subject was calculated,
and the mean false detection rate (over all test subjects) was estimated. Similarly, the
95% confidence interval of the mean false detection rate was calculated as the BCa
bootstrap confidence interval by re-sampling the test subjects.
3. RESULTS
3.1. Training Performance
As described previously, the test detection algorithm was developed and trained in a
training dataset consisting of 47 long-tern scalp EEG recordings with a total of more
than 3,600 recording hours. This rich dataset allowed us to optimize the parameters
and detection criteria used in the
algorithm. The seizure events were
determined by the epileptologists at
the clinical sites. Figure 10 shows the
detection performance curve in this
training dataset that depicts the
relationship between the detection
sensitivity and false detection rste.
The probability threshold for deter-
mining the PMRS similarity (first
criterion in ARC) was used as the
varying parameter on the performance
curve. According to this curve, it was
determined that the parameter value
that gives the algorithm 83% detec-
tion sensitivity with a false detection
rate of 0.026/hour would be used in
the performance validation study.
3.2. Assessment of Detection Performance in Test Dataset
To avoid any potential bias from the algorithm development, assessment of the
detection performance was conducted in a separate test dataset consisting of 436 EEG
segments from 55 subjects with a total of 1208 recording hours. All EEG segments
were reviewed by three blinded, independent epileptologists to determine seizure
events. As a result, a total of 146 electrographic seizures were included in the
validation study. All detection criteria and their thresholds were fixed for all test
datasets.
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Fig. 10. Detection performance curve in the training
dataset
Total number of subjects: 47
Total number of seizures = 141
Total number of recording hours = 3652.54
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False Detection per Hour
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045
3.2.1. Detection Sensitivity. The number of true detections for each test subject from
the test algorithm is shown in Fig. 11 (no seizures were identified by the Review Panel in
9 subjects). Using the optimal parameter settings determined from the training dataset, in test
dataset, overall detection sensitivity was nearly 80% (79.45%) with its 95% BCa bootstrap
confidence interval (number of bootstrap re-sampling = 3,000) = [70%, 87%].
3.2.2. False Detection Rate. The false detection rate (per hour) for each test subject
is shown in Fig. 12. In this test dataset, the test algorithm generated false detections at a
mean rate of 0.086 per hour, with its bootstrap standard error equal to 0.02. The 95%
BCa bootstrap confidence interval (number of bootstrap re-sampling = 3,000) of the false
detection rate = [0.05, 0.14].
86 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
Fig. 11. The number of test events and the corresponding test algorithm’s true detections for all test subjects.
No seizures were identified by the EEG reviewers in nine subjects
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 3 2 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
Subject
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T
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Overall Detections Sensitivity = 116/146=0.795
Test Seizure Events
True Detections
Fig. 12. False detection rate (per hour) by the test algorithm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 3 2 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55
Subject
1.0
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a
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p
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Mean False Detection Rate = 0.086 per Hour
3.2.3. Performance Compa-
rison between the Test Algorithm
and Reveal Algorithm. The same
test dataset was processed by Reveal
using its default setting for scalp
EEG (perception score = 0.5) and
achieved an overall observed sensi-
tivity of nearly 81% (80.8%) with its
95% confidence interval [72%, 88%].
Using less sensitive settings (gene-
rating fewer false detections) with a
perception score = 0.8 and 0.9, the
observed sensitivity achieved 76%
(95% CI = [67%, 85%]) and 74%
(95% CI = [64%, 84%]), respec-
tively. Statistical comparison (by bootstrap distribution) of the detection sensitivity between
the test algorithm and Reveal suggests that they are not significantly different. With respect
to the false detection rate, with 0.5 perception score setting, Reveal generated false
detections at a mean rate of 0.55 per hour, with the 95% BCa bootstrap confidence
interval = [0.42, 0.71]. With a perception score = 0.8 and 0.9, Reveal performed a mean
false detection rate of 0.33 (95% CI = [0.24, 0.44]) and 0.24 (se = 0.93 with
95% CI = [0.17, 0.33]) per hour, respectively. Statistical comparison (by bootstrap distribution)
of the false detection rates suggests that the test algorithm performed with a significantly lower
false detection rate than Reveal (p � 005. for all three perception score settings).
Figure 13 shows overall detection performance (receiver operating characteristic,
ROC, curves) for both test and Reveal algorithm by changing the detection thresholds. It
is clear that the false detection rate of the test detection algorithm is significantly smaller
than that of Reveal algorithm at different level of detection sensitivities. Furthermore, the
difference becomes more and more significant when the detection sensitivity increases.
4. CONCLUSION
Multi-channel scalp EEGs are highly complex and nonstationary signals, both in time
and in space. This is due to the fact that EEG not only reflects patients’ brain
activities during different states, but also contains noise and artifacts from various
sources. By applying advanced mathematical tools and novel spatiotemporal pattern
recognition techniques, the aim of the present study was to develop a highly reliable
and clinically useful computer algorithm for offline seizure detection and
identification in multi-day long-term scalp EEG recordings. This algorithm processes
EEG recordings at a speed of approximately 50 times of real time on the standard
dual-core desktop computer. In other words, it will take less than 30 minutes to
process and report detections for a 24-h scalp EEG recording. Computer software
based on such an algorithm would greatly enhance the efficiency of long-term EEG
monitoring in Epilepsy Monitoring Units (EMU) and Intensive Care Units (ICU).
The seizure detection algorithm introduced in this report extracts various signal
characteristics from scalp EEG recordings and translates them into multiple EEG
descriptors sequentially in time across the brain recording sites (EEG channels). The
process decodes the original EEG recording into a 3-dimensional database. Based on this
database, the algorithm was trained to identify EEG epochs that contained ictal EEG
patterns. To reduce false detections, the algorithm was also taught to reject EEG epochs
that were dominated by common recording artifacts, including muscle and chewing
artifacts, or normal sleep EEG patterns. The validation study was conducted in a separate
test dataset in which each of the EEG segments was independently reviewed by three
blinded epileptologists. The overall results showed that the test detection algorithm was
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6 87
Fig. 13. Detection receiver operating characteristic, ROC,
curves for the test algorithm and Reveal algorithm
Test Algorithm
Reveal Algorithm
0.0 0.1 0.2 0.3 0.4 0.5 0.6
False Detection Rate per Hour
0.85
0.80
0.75
0.70
0.65
D
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able to detect 80% of the electrographic seizure activities agreed upon by the majority of
epileptologists with a mean false detection rate of just 2 per day (0.086 per hour). In fact,
more than half of the test subjects had a false detection rate of less than 1 per day. Assessed
under the same test dataset, Reveal, one of the most used clinical seizure detection software
products available, generated 13 false detections per day when its detection parameter was
adjusted to have similar detection sensitivity to the test algorithm.
The results of this study suggest that it is possible to identify different signal patterns
contained in complex scalp EEG recordings by mathematically decoding multi-channel
signals into quantitative descriptors that represent signal characteristics such as regularity,
amplitude, and frequency. These patterns include external artifacts and brain electrical
activities from sleep, muscle, movement, and most importantly, epileptic seizures. With
further configuration and training, such a system may also offer clinically useful
information about other brain disorders that can be diagnosed by scalp EEG monitoring.
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Ïîñòóïèëà 22.10.2009
88 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2010, ¹ 6
|
| id | nasplib_isofts_kiev_ua-123456789-45648 |
| institution | Digital Library of Periodicals of National Academy of Sciences of Ukraine |
| issn | 0023-1274 |
| language | Russian |
| last_indexed | 2025-12-01T12:33:16Z |
| publishDate | 2010 |
| publisher | Інститут кібернетики ім. В.М. Глушкова НАН України |
| record_format | dspace |
| spelling | Deng-Shan Shiau Halford, J.J. Kelly, K.M. Kern, R.T. Inman, M. Jui-Hong Chien Pardalos, P.M. Yang, M.C.K. Sackellares, J.Ch. 2013-06-17T06:34:46Z 2013-06-17T06:34:46Z 2010 Signal regularity-based automated seizure detection system for scalp EEG monitoring / Deng-Shan Shiau, J.J. Halford, K.M. Kelly, R.T. Kern, M. Inman, Jui-Hong Chien, P.M. Pardalos, M.C.K. Yang, J.Ch. Sackellares // Кибернетика и системный анализ. — 2010. — № 6. — С. 74–88. — Бібліогр.: 21 назв. — рос. 0023-1274 https://nasplib.isofts.kiev.ua/handle/123456789/45648 519.6 Розглянуто роботу автоматизованої системи реєстрації ЕЕГ головного мозку для раннього виявлення епілептичних нападів. Розроблено комп’ютерний алгоритм для перетворення складних багатоканальних сигналів ЕЕГ мозку на кілька динамічних показників, супроводжуваних дослідженнями їхніх просторово-часових властивостей. Робота алгоритму аналізується на великому клінічному наборі даних. The purpose of the present study was to build a clinically useful automated seizure detection system for scalp EEG recordings. To achieve this, a computer algorithm was designed to translate complex multi-channel scalp EEG signals into several dynamical descriptors, followed by the investigations of their spatiotemporal properties that relate to the ictal (seizure) EEG patterns as well as to normal physiologic and artifact signals. This paper describes in detail this novel seizure detection algorithm and reports its performance in a large clinical dataset. This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS) from NIH-NINDS ru Інститут кібернетики ім. В.М. Глушкова НАН України Кибернетика и системный анализ Системный анализ Signal regularity-based automated seizure detection system for scalp EEG monitoring Автоматизована система виявлення епілептичного нападу на основі безперервного аналізу сигналів для моніторингу електроенцефалограми головного мозку Article published earlier |
| spellingShingle | Signal regularity-based automated seizure detection system for scalp EEG monitoring Deng-Shan Shiau Halford, J.J. Kelly, K.M. Kern, R.T. Inman, M. Jui-Hong Chien Pardalos, P.M. Yang, M.C.K. Sackellares, J.Ch. Системный анализ |
| title | Signal regularity-based automated seizure detection system for scalp EEG monitoring |
| title_alt | Автоматизована система виявлення епілептичного нападу на основі безперервного аналізу сигналів для моніторингу електроенцефалограми головного мозку |
| title_full | Signal regularity-based automated seizure detection system for scalp EEG monitoring |
| title_fullStr | Signal regularity-based automated seizure detection system for scalp EEG monitoring |
| title_full_unstemmed | Signal regularity-based automated seizure detection system for scalp EEG monitoring |
| title_short | Signal regularity-based automated seizure detection system for scalp EEG monitoring |
| title_sort | signal regularity-based automated seizure detection system for scalp eeg monitoring |
| topic | Системный анализ |
| topic_facet | Системный анализ |
| url | https://nasplib.isofts.kiev.ua/handle/123456789/45648 |
| work_keys_str_mv | AT dengshanshiau signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT halfordjj signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT kellykm signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT kernrt signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT inmanm signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT juihongchien signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT pardalospm signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT yangmck signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT sackellaresjch signalregularitybasedautomatedseizuredetectionsystemforscalpeegmonitoring AT dengshanshiau avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT halfordjj avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT kellykm avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT kernrt avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT inmanm avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT juihongchien avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT pardalospm avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT yangmck avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku AT sackellaresjch avtomatizovanasistemaviâvlennâepíleptičnogonapadunaosnovíbezperervnogoanalízusignalívdlâmonítoringuelektroencefalogramigolovnogomozku |