A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings
The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of conv...
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Інститут кібернетики ім. В.М. Глушкова НАН України
2011
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| Cite this: | A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings / Ch. Jui-Hong, Sh. Deng-Shan, J.J. Halford, K.M. Kelly, R.T. Kern, M.C.K. Yang, Zh. Jicong, J.Ch. Sackellares, P.M. Pardalos // Кибернетика и системный анализ. — 2011. — Т. 47, № 4. — С. 95-107. — Бібліогр.: 41 назв. — рос. |
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nasplib_isofts_kiev_ua-123456789-842192025-02-09T13:26:13Z A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings Алгоритм автоматизованого прогнозування епілептичного нападу на основі аналізу регулярності сигналів, що використовує тривалі інтервали записів електроенцефалограми головного мозку Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. Системный анализ The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure. Запропоновано алгоритм автоматизованого прогнозування епілептичного нападу на основі аналізу регулярності сигналу ЕЕГ головного мозку. Регулярність сигналу розраховується на основі введеної величини регулярної статистики збігу фрагментів (Pattern Match Regularity Statistics — PMRS). Відмінною рисою алгоритму є ступінь збіжності в значеннях PMRS, розрахованих на основі показань із різних груп електродів, визначених у процесі навчання алгоритму. В основі алгоритму лежить гіпотеза про те, що збіжність у значеннях величини PMRS збільшується під час переходу в стан нападу і в такий спосіб може слугувати індикатором для прогнозування нападу. 2011 Article A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings / Ch. Jui-Hong, Sh. Deng-Shan, J.J. Halford, K.M. Kelly, R.T. Kern, M.C.K. Yang, Zh. Jicong, J.Ch. Sackellares, P.M. Pardalos // Кибернетика и системный анализ. — 2011. — Т. 47, № 4. — С. 95-107. — Бібліогр.: 41 назв. — рос. 0023-1274 https://nasplib.isofts.kiev.ua/handle/123456789/84219 519.6 en Кибернетика и системный анализ application/pdf Інститут кібернетики ім. В.М. Глушкова НАН України |
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Системный анализ Системный анализ |
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Системный анализ Системный анализ Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings Кибернетика и системный анализ |
| description |
The purpose of this study was to evaluate a signal regularity-based automated seizure prediction algorithm for scalp EEG. Signal regularity was quantified using the Pattern Match Regularity Statistic (PMRS), a statistical measure. The primary feature of the prediction algorithm is the degree of convergence in PMRS (“PMRS entrainment”) among the electrode groups determined in the algorithm training process. The hypothesis is that the PMRS entrainment increases during the transition between interictal and ictal states, and therefore may serve as an indicator for prediction of an impending seizure. |
| format |
Article |
| author |
Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. |
| author_facet |
Jui-Hong, Ch. Deng-Shan, Sh. Halford, J.J. Kelly, K.M. Kern, R.T. Yang, M.C.K. Jicong, Zh. Sackellares, J.Ch. Pardalos, P.M. |
| author_sort |
Jui-Hong, Ch. |
| title |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_short |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_full |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_fullStr |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_full_unstemmed |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings |
| title_sort |
signal regularity-based automated seizure prediction algorithm using long-term scalp eeg recordings |
| publisher |
Інститут кібернетики ім. В.М. Глушкова НАН України |
| publishDate |
2011 |
| topic_facet |
Системный анализ |
| url |
https://nasplib.isofts.kiev.ua/handle/123456789/84219 |
| citation_txt |
A signal regularity-based automated seizure prediction algorithm using long-term scalp EEG recordings / Ch. Jui-Hong, Sh. Deng-Shan, J.J. Halford, K.M. Kelly, R.T. Kern, M.C.K. Yang, Zh. Jicong, J.Ch. Sackellares, P.M. Pardalos // Кибернетика и системный анализ. — 2011. — Т. 47, № 4. — С. 95-107. — Бібліогр.: 41 назв. — рос. |
| series |
Кибернетика и системный анализ |
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UDC 519.6
JUI-HONG CHIEN, DENG-SHAN SHIAU, J.J. HALFORD, K.M. KELLY, R.T. KERN,
M.C.K. YANG, JICONG ZHANG, J.Ch. SACKELLARES, P.M. PARDALOS
A SIGNAL REGULARITY-BASED AUTOMATED SEIZURE PREDICTION
ALGORITHM USING LONG-TERM SCALP EEG RECORDINGS
1
Keywords: epileptic seizure, seizure warning, scalp electroencephalogram, brain
dynamics.
1. INTRODUCTION
An epileptic seizure is a transient occurrence of signs and/or symptoms due to
abnormal excessive and synchronous neuronal activity in the brain [1]. The
worldwide prevalence of epilepsy ranges from 0.4% to 1% [2]. Some epileptic
patients experience a prodrome or an aura [3], which can serve as a warning before
frank signs of seizure onset. Rare patients learn to abort a seizure without external
intervention [4]. However, most patients cannot predict or arrest their seizures. In
industrialized countries, where antiepileptic drugs and seizure control devices are
readily available, about 70% of epilepsy patients are able to gain satisfactory
control of their seizures [5]. For patients whose seizures do not respond to
antiepileptic medications, less than 50% are candidates for epilepsy surgery [6].
Therefore, approximately 15–20% of epilepsy patients have no choice but to live
their lives with unforeseen and uncontrolled seizure attacks, which cause
considerable stress for these patients and their care-givers and limit the range of
daily activities available due to safety concerns. These lifestyle limitations decrease
quality of life and may contribute to the increased prevalence of depression in
patients with uncontrolled seizures [7]. If a device could be developed that could
warn an epilepsy patient of an impending seizure, it could lessen the psychological
stress of epilepsy and improve patient safety.
Results from several studies based on the analysis of intracranial EEG [8] and
fMRI [9] data suggest the existence of a preictal transition between an interictal and
ictal state. However, detecting a preictal transition using EEG signals from scalp
electrodes may be more difficult due to the attenuating, spatial distortion, and filtering
effects of the skull and soft tissues. Nevertheless, if a seizure warning system can be
developed for scalp EEG, it could have a wide range of clinical diagnostic and
therapeutic applications due to its portability, relatively low cost, and safety.
Scalp EEG has been used to investigate normal and pathological brain function
for over 80 years, when Hans Berger published his work and coined the term
“Elektenkephalogram” in 1929 [10]. Early attempts to predict seizures from EEG
signals began in the 1970s and flourished during the 1990s [11]. Because of the
non-linear nature of neuronal function and the paroxysmal character of a seizure,
non-linear signal processing methods were popularly applied to EEG for predicting
seizures [12–16]. By applying a non-linear similarity index, Le Van Quyen, Martinerie
and colleagues studied preictal EEG dynamics in 26 scalp EEG recordings obtained
from 23 patients with temporal lobe epilepsy. In five patients with simultaneously
scalp and intracranial recordings, changes of similarity index values was observed
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4 95
1This work was supported by the grants 5R01NS050582 (JCS) and 1R43NS064647 (DSS)
from NIH-NINDS.
© Jui-Hong Chien, Deng-Shan Shiau, J.J. Halford, K.M. Kelly, R.T. Kern, M.C.K. Yang, Jicong Zhang,
J.Ch. Sackellares, P.M. Pardalos, 2011
during preictal period in both types of EEG recordings, and 25 out of the 26 EEG
segments showed changes prior to the occurrence seizures (mean 7 minutes). Although
this study did not evaluate the specificity of the similarity index change in long-term
EEG recordings (contained only 50 minutes before seizure onsets), it rendered an
encouraging result using only scalp EEG to achieve seizure prediction [17], and as is
known, various prediction models were widely used in univariate and bivariate
analysis [41]. Another study by Hively and Protopopescu used L1-distance and �
2
statistic to estimate the dissimilarity in density functions between the base-windows
and the test-windows in 20 scalp EEG recordings [18]. The result showed pre-seizure
changes in all datasets with the forewarning times ranged from 10 to 13660 seconds.
However, this study only examined one selected channel in each data set, and similar
to the Le Van Quen study [17], specificity of the method was not reported. Two
follow-up studies by the same group, one used all available recording channels and the
other used a fixed channel, showed similar results [19, 20]. While the results seemed
promising, no validation study has been reported for this method. In the study reported
by Corsini, Shoker and coworkers, 20 sets of simultaneously scalp and intracranial
EEG recordings were analyzed using blind source separation and short-term Lyapunov
exponent (STLmax) [12, 21]. They observed changes of STLmax values before seizure
onsets and that the scalp EEG may give better predictive power over intracranial EEG
when the intracranial electrodes did not record the electrical activity in the epileptic
focus. However, the practicality of this method on long-term scalp EEG recordings
may be limited due to the lack of an automatic procedure to select the most relevant
source component. Schad et al. investigated seizure detection and prediction in
423 hours of long-term simultaneously scalp and intracranial EEG recordings from
six epileptic patients [22]. The method used techniques based on simulated leaky
integrate-and-fire neurons. The study reported that 59% (50%) of the 22 seizures were
predicted using scalp (invasive) EEGs given a maximum number of 0.15 false
predictions per hour. In the study by Bruzzo et al., a small sample of scalp EEG
recordings (115 hours from 3 epileptic patients) was analyzed using permutation
entropy (PE) [23]. By examining the area under the receiver operating characteristic
(ROC) curve, they reported that the decrease of PE values was correlated with the
occurrence of seizures. However, the authors also concluded that the dependency of
PE changes on the vigilance state may restrict its possible application for seizure
prediction. More recently, Zandi, Dumont and colleagues reported a prediction method
based on the positive zero-crossing interval series [24]. The method was applied on a
21.5 hour scalp EEG dataset recorded from 4 patients with temporal lobe epilepsy.
They reported a training result of 87.5% sensitivity (16 seizures) with a false
prediction rate of 0.28 per hour, and the average prediction time was approximately
25 min. James and Gupta analyzed long-term continuous scalp EEG recordings from
nine patients (5 in training set and the other 4 in test dataset) [25]. The data were
processed by a sequence of techniques consisting of independent component analysis,
phase locking value, neuroscale, and Gaussian mixture model. The prediction
performance of this method achieved a sensitivity of 65–100% and specificity of
65–80% as the prediction horizon ranged from 35–65 minutes in the test dataset.
One of the common features used in many of the seizure warning algorithms is the
change of synchronization of EEG signals recorded from different electrode sites. The
concept of “synchronization” can be quantified in several different ways such as
coherence [26], phase synchronization [27], or entrainment of dynamic features
(denoted as “dynamic entrainment” hereafter) [28]. In this study, we have attempted to
identify preictal transitions by detecting dynamic entrainment based on the
96 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4
convergence of PMRS among multiple electrode sites. PMRS is a probabilistic statistic
that quantifies the regularity of a time series. It is especially useful when the moment
statistics (e.g., mean, variance, etc.) or frequency cannot detect changes in a signal. By
further applying paired t-statistic (denoted as “T-index” hereafter) that quantifies the
convergence of two PMRS time series over time, we constructed an automated seizure
prediction algorithm that monitors the change of T-index and issues a warning of an
impending when the T-index curve exhibits the pattern defined by the algorithm. The
general hypothesis is that seizures are preceded by PMRS entrainment; this hypothesis
was based upon findings reported previously using a different measure of signal order,
STLmax [12, 29–31, 40]. The prediction parameters and the specific EEG channels to
be monitored were determined by the use of a training dataset. Algorithm performance
was then assessed using an independent test dataset. The performance was further
validated by comparison with that from a random warning scheme that did not use any
information from the EEG signals.
2. METHODS
2.1. Data Characteristics
2.1.1. Subjects and EEG recording specifications. All subjects were 18 years of
age or older admitted to either Allegheny General Hospital (AGH, Pittsburgh, PA)
or the Medical University of South Carolina (MUSC, Charleston, SC) for inpatient
seizure monitoring for diagnostic purposes or presurgical evaluation. Data
collection procedure was approved by the Investigational Review Boards of AGH
and MUSC, and the Western Investigational Review Board (WIRB). The EEG
recordings at MUSC were obtained using XLTEK monitoring systems (Oakville,
Ontario, Canada) with a sampling rate of 256 Hz and the EEG recordings at AGH
used 128-channel Nicolet BMSI-6000 systems (Viasys, Madison, WI, USA) with
a 400 Hz sampling rate. The EEGs recorded at both institutions used a referential
montage and the 19-electrode international 10–20 system of electrode placement.
The exact locations of referential electrodes placed in our dataset were decided
on-site and usually followed the recommended location of the Cz and Pz electrodes
as suggested by the American Clinical Neurophysiology Society [32]. All segments
were reviewed by the collaborative clinical sites (AGH and MUSC) and all seizure
events were verified by KK and JH, respectively.
2.1.2. Data selection. Because a scalp EEG might be severely contaminated with
artifact, typical muscle contraction and movement artifacts include blinking, chewing,
and talking, only EEG segments with a tolerable level of artifact, i.e., not present for
more than 50% of the recording and not involving more than 50% of the recording
channels, were included in this study. All EEG segments were long-term recordings
(mean = 24.4 hours) containing at least one seizure. EEG segments containing more
than one seizure within any two-hour interval were excluded to avoid potential overlap
between preictal and postictal periods of consecutive seizures. Thus, the warning
algorithm had a sufficiently long period of observation to detect the transition from
interictal to ictal states. The resultant dataset of EEG recordings included in this study
was collected from 52 patients. The dataset was randomly divided into training ( )n � 21
and test ( )n � 31 sets. Both training and test sets had more than 40 seizures (43 and 60,
respectively). Subjects from each clinical site had a similar proportion of seizures in
the two datasets. The individual recording durations of each subject in the training
dataset and test dataset are shown in Fig. 1 and Fig. 2, respectively. The mean
recording duration for each subject in the training dataset was 39.78 hours and that in
the test dataset was 33.54 hours. The standard deviation of recording duration was
22.96 hours in the training dataset and 15.99 hours in the test dataset. The shortest
recording duration was 6.18 hours and the longest duration was 92.41 hours.
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4 97
2.2. Pattern-Match Regularity Statistic (PMRS)
PMRS is a probabilistic statistic quantifying signal regularity [33, 34]. One of the
characteristic features of EEG signals during a seizure is the rhythmic and regular
discharges over a wide range of the brain. Therefore, the first step of the warning
algorithm presented in this study calculated PMRS sequentially for each EEG
signal analyzed. The rationale of applying this pattern match method (instead of
value match) is due to its robustness over scalp signal values, which are usually
more unstable than their up-and-down trends. The procedure for calculating PMRS
is described below.
Given a time series U u u un� { }1 2, , ,� with standard deviation �� n , a tolerance
coefficient e, and a fixed integer m, the two segments inU (x u u ui i i i m� � � �{ }, , ,1 1� ,
x u u uj j j j m� � � �{ }, , ,1 1� ) are considered pattern-matched to each other when:
98 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4
Fig. 1. Recording duration of all 21 subjects in the training dataset. The range of recording duration was
between 6.18 to 92.41 hours. The mean was 39.78 hours and the standard deviation was 22.96 hours
Fig. 2. Recording duration of each subject in the test dataset. The range of recording duration was be-
tween 15.16 to 71.21 hours. The mean was 33.54 hours and the standard deviation was 15.99 hours
{ } { }| | � | | �u u e u u ei j n i m j m n� � � � � �� � � �� �1 1
� � � � � �� � � � � �{sign sign }( ) ( ) , , , ,u u u u k mi k i k j k j k1 1 1 2 1� . (1)
The first two criteria require value match to some extent at both the beginning and
ending points of two segments, where e was set to be 0.2 empirically. The third
criterion requires pattern match between xi and xj within a range of m (set as 3 in this
study). To calculate PMRS, we first define a conditional probability:
p u ui i m i m� � �� � �Pr{sign( )1
= sign and are pattern match}( ) |u u x xj m j m i j� � �� 1 . (2)
Given m pi, can be estimated as: for 1 � � �j n m,
�
# [ ] [ (
p
x x u u
i
j i i m i m
�
� � �� � �of { s pattern match with sign 1 ) ( )]
#
,
� �
�
� � �sign }
of { s pattern match with }
u u
x x
j m j m
j i
1
(3)
where 1 � � �i n m, and finally,
PMRS � �
� �
�
�
1
1n m
pi
i
n m
ln ( � ). (4)
As the time series U develops into a more regular state, �pi s become larger and
PMRS decreases as a result.
2.3. PMRS Entrainment (T-index)
T-index is basically the paired t-statistic function used to quantify the degree of
entrainment between two PMRS time series. The specific calculation is as follows.
For two time series X i and X j (the PMRS value time series), if their values in a
calculation window W t with a size of n data points are presented as:
L X X Xi
t
i
t
i
t
i
t n� � � �{ }, , ,1 1
� , (5)
L X X Xj
t
j
t
j
t
j
t n� � � �{ }, , ,1 1
� (6)
then the pair-wise differences between Li
t and L j
t can be written as:
D L L X X X X X Xij
t
i
t
j
t
i
t
j
t
i
t
j
t
i
t n
j
t n� � � � � �� � � � � �{ , , ,1 1 1
�
1} �
= { }d d dij
t
ij
t
ij
t n, , , .� � �1 1
� (7)
The T-index over the calculation window W t between the two time series is
calculated by:
Tind D
n
ij
t
ij
t Dij
t
� | |
�
,
�
(8)
where Dij
t and ��
Dij
t are the sample mean and the sample standard deviation of Dij
t ,
respectively. PMRS entrainment is a process during which one EEG signal is
influenced by or coupled with another with respect to the signal regularity. This
phenomenon was used as a dynamical pattern for warning of an impending seizure.
Fig. 3 shows the PMRS traces derived from three EEG channels (F8, T4, and T6)
(upper panel) and their average T-index values (bottom panel) over a 350-minute
interval containing a seizure. As shown in the PMRS plot, all PMRS values of the
ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4 99
three channels dropped seconds after the seizure onset (indicated by a black vertical
line), which was due to the extreme signal singularity during the ictal period. More
importantly, the PMRS values became convergent about 60 min before the seizure
onset, which caused the decrease of T-index values (shown in T-index plot).
2.4. Seizure Warning Mechanism
2.4.1. Overview. EEG signals were first filtered by a fifth-order Butterworth filter
with a band-passing frequency between 1 to 20 Hz (the bandwidth within which
most ictal epileptiform patterns occur) [35]. After the filtering process, for each
channel, PMRS was calculated for each non-overlapping 5.12-second epoch. Based
on the PMRS values, T-indices were then calculated for each of the selected
channel groups (three channels each). To increase the sensitivity of seizure
warning, the proposed algorithm independently monitored four T-index curves (i.e.,
from four channel groups). A warning was issued when any of the monitored
T-index curves met entrainment criteria.
2.4.2. Selection of channel groups forseizure warning monitoring. Channel
groups were selected such that they were bilaterally symmetric along the midsagittal
line. In order to avoid frequent eye movement artifact, electrodes Fp1 and Fp2 were
excluded. In addition, to avoid the regular alpha rhythm pattern that might give
potential false positive warnings because of the nature of the regularity statistics used
in our prediction algorithm, electrodes O1 and O2 were also excluded.
The four channel groups selected in this study resemble the well known and
popular anterior-posterior bipolar (“double banana”) montage. These four channel
groups were: (F7, T3, T5), (F3, C3, P3), (F4, C4, P4), and (F8, T4, T6). The main
rationale for this selection was that most seizures occurring in patients with temporal
lobe epilepsy start from a channel group on one side (hemisphere) of the brain, and
therefore, intuitively, these channels were more likely to be entrained with each other
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Fig. 3. Dynamic features of three EEG electrode signals. Top: PMRS traces of F8, T4, and T6 elec-
trodes. There is a sudden drop of PMRS values in all three channels right after the seizure onset (denoted
by vertical dashed black lines in both panels at the 200-min time point). Bottom: averaged T-index
among the three channels. Approaching the time of seizure occurrence, a gradual decrease of the T-index
(entrainment) from approximately the 120-min point to the 160-min point can be observed. The T-index
values remain small before the seizure
during the preictal transition period. By monitoring four channel groups that cover
both hemispheres, the algorithm was enabled to detect a PMRS entrainment that
preceded an impending seizure initiated from either hemisphere.
For each channel group k k, ,�1 4, the group T-index over the calculation
window W t is denoted as:
GTind
Tind
k
t
ii
t
i ii
�
�
�
�
�
�� ��
��
1
3
1
2
3
. (9)
The window size for calculating one group T-index is 60 PMRS data points, with
59 points overlapping from t to t �1 (i.e., sliding window). The warning algorithm only
monitored the four group T-index curves instead of individual pair-wise T-indices.
2.4.4. Detection of PMRS entrainment. The proposed prediction algorithm,
instead of attempting to detect a signal threshold crossing, was set to detect a certain
pattern of T-index dynamics that gradually descends from a baseline value. Therefore,
the first step to detect such a pattern was to determine an upper threshold U T (i.e.,
baseline value, see an illustration in Fig. 4). A decrease of a group T-index from U T
was considered as a necessary condition for a potential PMRS entrainment, and an
entrainment was identified when the group T-index values fell below a lower threshold
LT . It was further defined that a period of entrainment should be maintained for at least
several minutes. For each group T-index, its U T was set to be the asymptotic 95th
percentile in the preceding 12 min, as described in equation 10. The duration of 12 min
was decided by training across multiple patients in the training dataset:
U T t T tT � � �mean std( ( )) ( ( ))min min12 122 . (10)
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Fig. 4. Resized plot of Fig. 3 with warning algorithm parameter indications. The seizure warning sensi-
tivity relates to parameters tt and D. As the algorithm finds both LT and UT , the difference of time and
the group T-index between them, as indicated by Tt and d in the figure, are compared with tt and D, re-
spectively. Only the decrease d of a T-index curve was larger than D, and also tt larger than tt would
trigger a seizure warning. For example, with the proper setting of tt and D, the T-index drop at 100 min
was not considered a warning event because a large d occurred during a short Tt. However, the T-index
drop from the 120-min point to the 160-min point shows a gradual and persistent decrease and is consid-
ered a proper warning event. The dashed black line denoted the seizure onset time point
Issue Seizure Warning
LTLT
LT
U T
U T
Tt
Tt
d
d
However, if half of the following 16 T-index values (representing 81.92 seconds
of data) were greater than U T , U T was updated as the median of these 16 values. This
operation is described in equations 11 and 12:
U U I A T I AT T� � � � �( ( )) ( ) ( ),1 median (11)
A H T t K U T
k
� � � �
�
�
��
� [ ( ) ] ,
1
16 16
2
(12)
where I ( )� is an indicator function, A is the criterion of updating U T , and T t( ) is
the group T-index value at time t. If A is false, then U T is kept as it is.
H ( )� denotes the Heaviside step function. U T is not updated with a maximum
value because the existence of artifact in raw EEG recordings could affect the
T-index causing an abrupt surge and therefore produce an abnormally high U T .
Once U T is determined, LT is equal to U T minus D, where D is a parameter that
would determine the sensitivity of the algorithm. In general, the bigger D is, the
less sensitive the algorithm will be, but the less susceptible the algorithm will be to
false warnings.
In addition to the above detection rules, the period of a descending T-index
pattern from U T to LT must continue for more than tt minutes to be regarded as
a PMRS entrainment. Therefore, the algorithm would issue a warning of an impending
seizure only when any of the monitored T-index curves traveled from U T to LT with
the traveling time longer than tt to ensure a gradual descendent pattern.
Once an entrainment was identified, other entrainments that followed (identified
within the seizure warning horizon (SWH) by the warning algorithm using the same
parameter settings) were silenced due to the possibility that an entrainment preceding
an onset may be much longer than the tt value and cause several entrainment
identifications.
The following flow chart gives an overview of our seizure warning algorithm.
102 ISSN 0023-1274. Êèáåðíåòèêà è ñèñòåìíûé àíàëèç, 2011, ¹ 4
Recording
end?
Yes
Performance
evaluation
Yes
Find the time when T-indices cross LT
Time that
T-indices fall
from UT to LT
longer than tt?
Issue a warning
No
Update UT
Yes
Calculating PMRS
Find next UT in each TSPG
Calculating T-indices
Calculating TSPG
T-indices
Are 50% of the
following 16 T-indices
bigger than UT?
Filter EEG signal
No
No
Fig. 5. Flow chart of the seizure warning mechanism
2.5. Statistical Evaluation
2.5.1. Estimation of performance statistics. The performance of the seizure warning
algorithm varies with the length of the seizure warning horizon (SWH) — the longer
the SWH, the lower the sensitivity and false positive rate (FPR). In this work, SWH
was defined as the time window following a seizure warning during which the
patient was likely to have a seizure [31]. It is worth noting that Winterhalder et al.
[36] defined the SWH (same as seizure prediction horizon, SPH) differently as
a short intervention preparation period. The definition of SWH here is closer to the
“seizure occurrence period” defined by this group. A seizure warning was considered
true only when at least one seizure onset occurred within the SWH following the
warning. Otherwise, the warning was considered a false positive.
After determining the outcome (i.e., true or false) of each warning, sensitivity was
estimated by dividing the number of correct warnings by the total number of seizures in the
EEG segments, i.e. the proportion of seizures correctly predicted (within SWH). FPR (per hour)
was calculated by dividing the total number of false positives by the total recording hours
outside the SWH before each seizure. The SWH period before each seizure was excluded
because it was impossible for a false warning to occur within the SHW before each seizure.
2.5.2. Statistical validation — comparison with a random seizure warning
scheme. Several methods have been proposed for validating the performance of a seizure
warning algorithm [37–39]. Each study tested a specific statistical hypothesis. In this
study, we compared the prediction sensitivity estimates with a random prediction scheme
that issued random seizure warnings (in time) during the recording. Under this random
scheme, the interval between two consecutive warnings followed an exponential
distribution, with a condition that the length of any interval could not be smaller than the
SWH. This additional condition ensured that the two compared algorithms (test
algorithm and prediction scheme) were consistent in terms of the management of nearby
warnings (within the SWH). Although this condition diminished some randomness from
the random prediction scheme, it was necessary in order to have a meaningful
comparison of prediction performance between the two prediction methods.
In order to have an unbiased comparison for sensitivity, two parameters were
controlled: 1) length of the SWH, and 2) total number of warnings. For each of the test
EEG segments, the total number of warnings allowed by the random predictor was set
to be the same as that issued by the test algorithm. For example, if the test algorithm
issued two true positive and two false warnings in a specific segment, the random
predictor would only randomly issue a total of four warnings. Under the same number
of warnings allowed, the study compared overall prediction sensitivity across all test
patients. A distribution of overall sensitivity by the random prediction scheme was
generated from 1000 simulations, and was used to assess how significant the prediction
performance by the test algorithm was over the random scheme.
It is worth mentioning that it was necessary to impose the above conditions in the
random warning algorithm compared in this study to make it comparable with the
proposed algorithm. Since this was a conditioned random process, the specific null
hypothesis tested in this study was: “The overall sensitivity (or FPR) of the test
algorithm is the same as that of a random prediction scheme with the same number of
warnings issued by the test algorithm.”
3. RESULTS
3.1. Training Results
The proposed algorithm was applied on the training dataset ( )n � 21 to optimize
settings for the parameters D and tt . Parameter D sets the minimal requirement that
a group T-index value needs to decrease from U T to be considered as a PMRS
entrainment, and tt gives the constraint on the minimal time length for a T-index
curve traveling from U T to LT . The training results are presented in Fig. 6, which
shows the receiver operating characteristic (ROC) curves (sensitivity vs. false
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positive rate) under different tt , ranging from 10 to 60 minutes with 10-minute
increments. Each ROC curve contains eight settings of parameter D, each
representing the performance under a certain D value, ranging from 1 to 8 (in
T-index units) with increments of 1. As D increased, each ROC curve first reached
its peak sensitivity and then declined as D kept increasing. This was due to the
constraint of tt : when D was set to a small value, Tt , the time interval during
which a T-index curve drops from U T to LT , was likely to be shorter than the
minimally required tt , and therefore the drop of the T-index curve could not be
considered as a PMRS entrainment. As D became bigger, the sensitivity started
increasing until the D value became too large to start affecting the number of
T-index drops (i.e., sensitivity started to reduce).
In this training study, parameters were optimized such that the test algorithm
achieved sensitivity of at least 70% with the lowest possible false positive rate. As
a result, the best prediction performance was obtained when tt was set to 20 minutes
and D was equal to 6 T-index units. These parameters were fixed for the performance
evaluation in the test dataset.
3.2. Test Results
The test dataset consisted of long-term EEG segments from 31 patients with a total
of 60 seizures. With the parameter settings determined in the training dataset, the
test algorithm gave 41 correct warnings among these segments. Using the
prediction parameters obtained in the training study, the overall sensitivity was
68.3% with an overall false positive rate of 0.235 per hour.
The random warning scheme was designed to issued random warnings with the same
number as that issued by the test algorithm for each segment in the test dataset. Each
random trial ran through all of the patients in the test dataset, and an overall sensitivity was
calculated. The random scheme repeated 1000 times on the test dataset and thus generated
1000 overall sensitivity estimates. Its (empirical) distribution is shown in Fig. 7.
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Fig. 6. Training results. ROC curves are shown under different tt values (tt � 40 50 60, , results are not
shown in this figure). Each point in the ROC curve corresponds to an overall result over the entire train-
ing dataset with a specific D value. The inset indicates the parameter tt used in each ROC curve. For ex-
ample, tt10 denotes the ROC curve using parameter tt � 10 min. The best parameter configuration was
observed at the tt value equal to 20 min, which achieved a sensitivity above 0.7 with a false positive rate
of 0.225 per hour
The overall sensitivity achieved by the test algorithm was then compared with the
sensitivity distribution of the random warning scheme. The sensitivity of the test
algorithm exceeded the sensitivity of 91% of the random warning trials
(p-value = 0.09). Fig. 7 also presents this comparison.
4. DISCUSSION
This paper presents an automated algorithm that issues seizure warnings by
monitoring the convergence of signal regularity among EEG channels of continuous
long-term scalp EEG recordings. The algorithm was developed with a training
dataset and its performance was evaluated using a separate test dataset. In the test
dataset, the algorithm achieved a sensitivity of 68% with a false positive rate of
0.235 per hour. The algorithm performance in the test dataset was nearly the same
as that in the training dataset. This implies that the algorithm generated stable
performance on epileptic patients. The overall sensitivity achieved by the test
algorithm was better than a random warnings process with a 91% confidence level
(p-value = 0.09). While these results are encouraging, the performance of the
algorithm may not be sufficient for some clinical applications for which greater
sensitivity or lower false positive rates are required. In our study, we use a single
parameter setting for all patients. In a real world application, however, such as
control devices, it may be possible to tune the algorithm, by adjusting specific
parameters to achieve better results in individual patients. For example, the vagus
nerve stimulator (VNS) stimulation parameters are adjusted on a patient-by-patient
basis, as is the deep brain stimulator (Medtronic) for Parkinson’s disease. Similarly,
the Neuropace seizure control device is adjusted with respect to epileptiform
discharge detections on a patient-by-patient basis, to achieve the best results
possible for each patient. In that sense, the study in this paper is proof of concept,
demonstrating that prediction from scalp EEG is possible.
The result of this study should not be interpreted to implythat the performance of
the proposed algorithm outperformed any random prediction scheme. As mentioned in
section 2.5.2, the study tested a specific null hypothesis based on the random
prediction scheme designed. In fact, a comparison of performance with a completely
random prediction scheme may have very little significance, both scientifically and
practically. Certain conditions should be imposed on the random scheme to prevent
potential bias in the comparison, especially for the assessment of a sophisticated
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Fig. 7. The distribution of the overall sensitivities achieved by the random warning scheme (generated
from 1000 trials). The overall sensitivity of the test algorithm is denoted by the dashed black vertical
line, which was better than 91% of the trials by the random scheme
EEG-based prediction algorithm. Nevertheless, there are several studies that have applied
different random warning schemes or surrogate data to test different hypotheses [22,
37–39]. The comparison of the proposed algorithm with other random warning schemes
should be followed. Furthermore, studies on a larger sample size and the assessment of the
proposed algorithm with different SWHs should be completed in the future.
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