Phase Lag Index Histogram Features for Identifying
Epileptic Seizure EEG Signals
Masashi Nishiyama∗ , Yoshio Iwai∗ , Masayoshi Oguri† and Yoshihiro Maegaki‡∗ Graduate School of Sustainability Science, Tottori University, Japan† Faculty of Health Sciences, Kagawa Prefectural University Of Health Sciences, Japan‡ Faculty of Medicine, Tottori University, Japan

Abstract—Recent medical analytical studies have focused on
the phase lag index (PLI) to analyze the functional connectivity ofthe brain. This paper assumes that the PLI contains informative
characteristics for automatically identifying electroencephalogram (EEG) signals of epilepsy patients. We propose a methodfor distinguishing between epileptic seizure and non-seizure EEG
signals using PLI histograms acquired from the signals of ashort period, randomly sampled from longer recordings, indifferent brain regions. We demonstrate the ability of the method
to identify epileptic seizures by experiments on the publicly
available CHB-MIT Scalp EEG database.
Index Terms—Electroencephalogram signals, epileptic seizures,histogram features, phase lag index.

I. I NTRODUCTION
Epilepsy is a neurological disorder in which severe seizures
are sometimes caused by the abnormal activity of brain functions. The prevalence of epilepsy is quite high, at about one
person in 100. For this reason, a doctor may diagnose many
epilepsy patients every day, using EEG monitoring systems. In
general, the EEG signals are repeatedly recorded across several
days. In rare cases, the signals are recorded continuously, 24hours/day, during several days. In practice, a doctor directlyreads long-duration EEG recordings to find seizure characteristics. It is said that reading long EEG recordings takes
substantial additional time—about one third of the recording
time—and so it is a heavy burden for a doctor. There is
therefore a need for a support system for primary screening, todistinguish between seizure EEG signals and non-seizure EEG
signals and thereby assist a doctor in diagnosing epilepsy. For
such a system, it is essential to design a method for accuratelyidentifying seizure EEG signals.
To design a method for identifying epileptic seizure EEG
signals, we focus on the functional connectivity of the brain.
As described in [1], functional connectivity refers to thedegree of correlation in activity between spatially distant brain
regions. In the medical field, it has been reported that epilepsy
impairs the functional connectivity of the brain [2], [3].
Recently, medical analytical studies [4], [5] have reported thatthe use of the phase lag index (PLI) is effective for analyzing
the functional connectivity of the brain. However, these studies
did not consider epileptic seizure EEG signals. Furthermore,
there is no method for automatically distinguishing between
epileptic seizure EEG signals and non-seizure EEG signals
using the characteristics of the PLI.

We propose a method for distinguishing between epileptic
seizure and non-seizure EEG signals using PLI histograms.
Our method extracts PLI histogram features by applying a
bandpass filter and computing the PLI values of EEG signals
during randomly sampled short periods. Experimental results,
using the publicly available CHB-MIT Scalp EEG database,show that our method using PLI histogram features has theability to identify epileptic seizure EEG signals.
II. P HASE L AG I NDEX
Before explaining the detail of our histogram features, weexplain PLI [6] and how it is used for analyzing the functional
connectivity of the brain. Phase synchronization indices suchas coherence and phase-locking value are widely used to represent the degree of connectivity. However, these indices are
erroneously increased by volume conduction1 . This problem issolved by PLI, which is another phase synchronization index.
Stam et al. define the PLI value p between two signals asT

p=1X
sgn (∆φ(t)) ,T t=1(1)where ∆φ(t) = φm (t)−φn (t) is the phase difference between
the two signals, sgn(∆φ(t)) is the signum function of ∆φ(t),t ∈ {1, · · · , T } is the (discrete) time, T is the sampling
length of the signals, and φm (t) and φn (t) are the phasecomponents of the two signals. The range of ∆φ(t) is definedas −π < ∆φ(t) ≤ π. ∆φ(t) is computed from two complex
signals converted using Hilbert transform, as we describe indetail in (8). The range of p is 0 ≤ p ≤ 1. When p = 0, phasesynchronization does not appear or phase synchronization
appears only when ∆φ(t) is close to 0 or ±π. When p = 1,phase synchronization perfectly appears except when ∆φ(t) isclose to 0 or ±π. In general, p takes a large value when phasesynchronization between the two signals strongly appears.
III. O UR PLI H ISTOGRAM F EATURES
A. Overview
Here we outline our PLI histogram feature for distinguishing between epileptic seizure and non-seizure EEG signals.
To design the feature, we used knowledge of the diagnosis
1 Volume conduction: Suppose that neurons and synapses change theirelectrical potential because of activity. The potential change diffuses andreaches the electrodes on the scalp through the cerebrospinal fluid and skull.

denote the start time (m, n)k of the k-th pair k ∈ {1, · · · , S}
byAcquire an epoch (performed by doctor)
Apply a bandpass filter
Compute an analytic signal
Randomly set a pair of signals ofa short period from the analytic signal

NOn ∼ U(0, T1 − T2 ),(4)z m (t0 ) = zb,c (tc ),YESConstruct the PLI histogramn(5)0z (t ) = zb,d (td ),Fig. 1. Overview of generating our PLI histogram features.

provided by a doctor in a clinical setting. As described in [1],a doctor diagnoses encephalopathy by checking the functional
connectivity of the brain. Specifically, the doctor observesa pair of EEG signals over a short period, acquired fromdistant brain regions. To automatically extract features foridentifying epileptic seizure signals, using knowledge of thediagnosis, our method computes PLI values representing thefunctional connectivity of the brain from pairs of signals overshort periods, which are randomly sampled from long-duration
recordings of EEG signals. It then constructs histograms using
the PLI values acquired from the pairs of signals, as shownin Fig. 1. The details of our feature extraction method aredescribed below.
B. Procedure
To view EEG signals from an epilepsy patient, a doctor
uses an EEG monitoring system that has C connection channels. The doctor acquires an epoch of multi-channel EEG
signals with the time length T1 . First, our method simply
applies a bandpass filter to an epoch to extract signals inspecific frequency bands. This frequency band is specified
as b ∈ {δ, θ, α, β, γ}, where (δ: 0.5−4 Hz), (θ: 4−8 Hz),
(α: 8−13 Hz), (β: 13−30 Hz), and (γ: above 30 Hz). Suppose that we acquire an EEG signal xb,c (t) at discrete timet ∈ {1, · · · , T1 } in the c-th channel c ∈ {1, · · · , C} of anepoch. To compute the PLI values, we generate an analytic
signal zb,c (t) of the c-th channel of frequency band b, aszb,c (t) = xb,c (t) + j H(xb,c (t)) ,(3)where U(0, T1 − T2 ) represents the discrete uniform distribution that randomly generates a duration between zero andT1 − T2 .
We define the pair of signals, over a short period, between
the c-th channel and d-th channel (c 6= d) using the start time(m, n)k as

Compute a PLI valueof the pair of signals
Is the number ofpairs enough?

m ∼ U(0, T1 − T2 ),(2)

where j is the imaginary unit and H(xb,c (t)) represents theHilbert transform of xb,c (t).
To compare signals over a short period in the analytic signal
zb,c (t), we use the pair of signals acquired during a period oflength T2 (< T1 ). Our method performs random sampling until
S pairs are acquired, by repeating the following procedure. We(6)where t0 ∈ {1, · · · , T2 } is a discrete time, tc = m + t0 − 1,td = n + t0 − 1, and c, d ∈ {1, · · · , C} are the channels of theEEG signals.
Having randomly sampled S pairs of the short-period signals z m (t0 ) and z n (t0 ) with start time (m, n)k , we compute
m 0a PLI value for each. A PLI value pm,nb,c,d , relating z (t ) andn 0z (t ), is defined as
Tpm,nb,c,d =21 X
sgn (∆φ(t0 )) ,T2 0(7)t =1

∆φ(t0 ) = arg

z m (t0 )z n (t0 )kz m (t0 )kkz n (t0 )k!
,(8)

where arg(z) represents the argument of the complex numberz and z n (t0 ) represents the complex conjugate of z n (t0 ).
To generate the feature vector for identification, we computethe histogram of the PLI values acquired by the random
sampling. We define the feature vector as the PLI histogram h,to temporally and spatially compare the short-period signals
between the different channels. h is defined as
h=CCXX
 1
Thb,c,d , h2b,c,d , . . . , hN,b,c,d(9)c=1 d=c+1

where N is the number of bins in the histogram and hlb,c,d isthe frequency of the l-th bin. We count the frequency hlb,c,dusing a set consisting of pm,nb,c,d , defined in Eq. (7), asm,nhlb,c,d = card{pm,nb,c,d |al ≤ pb,c,d < al+1 },(10)

where card is an operator representing the number of elements
m,nin the set consisting of pm,nb,c,d under the condition al ≤ pb,c,d <al+1 . It is worth noting that al = −1 + 2(l − 1)/N . WhenNpm,nb,c,d = 1, we count it in hb,c,d . The total number of pairsof short-period signals is CS. We apply L1-normalization toensure that ||h||1 = 1.

Fp1
F7

T7

P7

Fp2

F3

Fz

F4

C3CzC4P3Pz

P4O1TABLE I
ACCURACY (%) OF E PILEPTIC S EIZURE I DENTIFICATION U SING PLIH ISTOGRAM F EATURES W HILE VARYING THE F REQUENCY BAND ,L ENGTH OF THE S HORT P ERIOD , AND N UMBER OF B INS

F8T8T6O2Frequency band bθ
αβ58.7±8.5
51.9±8.1
54.9±7.8δ
62.7±9.6γ48.5±6.7

Length of short period T2 (s)0.1
11053.2±6.5
54.9±10.758.1±10.4Fig. 2. Electrode alignment for recording EEG signals for bipolar EEG lead.
The blue line is the connection channel between electrodes.
255.2±8.0

Number of bins N
61055.6±10.955.4±10.6

IV. E XPERIMENTS
A. Dataset
To evaluate the accuracy of distinguishing between epileptic
seizure and non-seizure EEG signals using our PLI histogram
features, we used the publicly available CHB-MIT Scalp
EEG database [7]. This dataset contains EEG signals from
22 epilepsy patients (five males and 17 females, from 1.5
to 19 years old). The signals were annotated with the start
times of epileptic seizures. We set the time length of eachepoch T1 = 20 s. The sampling rate was 256 Hz. We had
to solve the problem that some EEG signals in the CHBMIT Scalp EEG database were recorded using a different
electrode alignment, so that they could not be used. To solve
this problem, we selected only EEG signals recorded using
the electrode alignment shown in Fig. 2. This alignment had
C = 18 connection channels. In addition, we removed thepatients whose signals contained fewer than seven epochs
annotated as seizures. To remove the effects of artifacts, wedid not use epochs with amplitudes exceeding 350 µV. Finally,
we used EEG signals of 11 epilepsy patients (two male andnine female, from 1.5 to 16 years old). For each patient, both
seizure and non-seizure EEG signals were recorded.
B. Experimental Results
We used the leave-one-patient-out cross-validation scheme
to evaluate the accuracy of distinguishing between epileptic
seizure and non-seizure EEG signals. Epochs of multi-channel
EEG signals for one patient were used as test samples, andthose for the remaining ten patients were used as training
samples. Using the linear support vector machine (SVM),
we compared the accuracy of the identification methods. We
conducted our evaluations with frequency bands for b = δ, θ,α, β, and γ signals, lengths of periods T2 = 0.1, 1, and 10 s,and numbers of bins N = 2, 6, and 10. We randomly sampled
S = 1, 000 pairs of short-period signals. In the process of
training the linear SVM classifier, we assigned epochs ofseizure EEG signals to positive samples, and epochs of nonseizure EEG signals to negative samples.
Table I shows the identification accuracy of our PLI histogram features while varying the frequency band b, the lengthof the short period T2 , and the number of bins N . We
calculated the average and standard deviation of the accuracy

values obtained when one parameter was fixed and the others
were changed. We can observe that the δ frequency band
yielded higher accuracy than the θ, α, β, and γ frequency
bands, and T2 = 10 s yielded higher accuracy than T2 = 0.1 sand 1 s. For the number of bins N , the accuracy was almost thesame for N = 2, 6, and 10. We obtained the highest accuracy,73.2% (sensitivity 58.2% and specificity 88.2%), using ourfeatures with the δ frequency band, T2 = 10 s, and N = 6.
C. Visualization
We investigated which PLI histogram features contributed
to enhancing the accuracy of identifying epileptic seizure EEG
signals. We used the parameters of the feature achieving thehighest accuracy, as described in Section IV-B. Figs. 3(a),(b), and (c) show the PLI histograms using δ signals forseizures and (d), (e), and (f) show those for non-seizures.
The PLI values of seizure signals of short periods in the δband were greater than zero more often than those of nonseizure signals. The pairs of signals taking large PLI values
contained discriminative characteristics in δ waves. We believethat there is a high probability of phase synchronization in theδ band of the seizure EEG signals. This result may suggest anassociation with the appearance of the spike-and-slow-wave
complex at about 3 Hz during seizures. Figs. 4(a), (b), and(c) show the PLI histograms using β signals for seizures,and (d), (e), and (f) show those for non-seizures. The PLIvalues of non-seizure signals of short periods in the β band
frequently became one. The pairs of signals whose value was
one contained discriminative characteristics in β waves. We
believe that the phase synchronization appeared in non-seizure
EEG signals of short periods in the β band. Because theseresults are not explained by existing medical knowledge, weneed to perform further investigation.
D. Comparison with Cross-Correlation Features
We compared the accuracy of our PLI histogram featureswith prevalent cross-correlation features [8] using the fiveelements of the cross-correlation values, computed from theEEG signals between channels. The prevalent cross-correlation
feature consisted of 5 × 153 = 765 dimensions because itFrequency

0.8

00

Frequency

0.8

Frequency

0.8

010

PLI
(a)

Frequency

0.8

Frequency

Frequency

PLI

011

PLI
(c)

0.8

00

PLI
(b)

0.8

00

10(d)

PLI

010(e)

PLI1(f)Fig. 3. Examples of PLI histograms of δ signals of patients experiencing epileptic seizures in (a), (b), and (c), and non-seizures in (d), (e), and (f).

Frequency

0.500Frequency

0.5

Frequency

0.5010PLI(a)PLI11Frequency

0.5

Frequency
00

PLI(c)

0.5

Frequency

0.5

001

PLI(b)

00(d)PLI(e)

100

PLI

1(f)

Fig. 4. Examples of PLI histograms of β signals of patients experiencing epileptic seizures in (a), (b), and (c), and non-seizures in (d), (e), and (f).

was computed from the five elements between 18 C2 = 153
combinations of channels. Both features were trained using alinear SVM classifier. We used the same dataset as describedin Section IV-A. Our PLI histogram features achieved anaccuracy of 73.2%, whereas the prevalent cross-correlation
features achieved 57.9%. Thus, our PLI histogram features
improved the accuracy of identifying epileptic seizure EEG
signals, compared with the prevalent cross-correlation features.
V. C ONCLUSIONSWe proposed a method for identifying epileptic seizure EEG
signals using PLI histogram features. By experiments on theCHB-MIT Scalp EEG database, we demonstrated that our PLI
histogram features improved identification accuracy. In futurework, we intend to evaluate the accuracy with a greater numberof epilepsy patients and analyze the experimental results froma medical perspective.
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