Band Correlation Histogram to Improve
Classification of Acute Encephalopathy in Infants
Takahiro Kondo∗ , Masashi Nishiyama∗† , Yoshio Iwai∗† , Masayoshi Oguri‡ and Yoshihiro Maegaki‡∗ Graduate School of Engineering, Tottori University† Cross-informatics Research Center, Tottori University‡ Faculty of Medicine, Tottori University

Abstract—We propose a method to determine whether the
early onset of acute encephalopathy causes severe sequela byanalyzing the frequency of electroencephalogram waves. Even
though sequela can severely damage the brains of infants, noprevalent method can automatically diagnose acute encephalopathy in them. We solve this problem by designing a discriminative
feature that delivers impressive classification performance. Basedon knowledge of the diagnosis, our method applies a bandpass
filter, randomly selects pairs of waves over a short period, andcomputes a band correlation histogram from a distribution of
their correlation coefficients. The results of experiments showthat the band correlation histogram is superior to the prevalentmethod in the classification of a dataset of patients with acuteencephalopathy.
Index Terms—EEG waves, acute encephalopathy, band correlation histogram

I. I NTRODUCTION
Impaired consciousness and convulsions are typical symptoms of acute encephalopathy in infant patients. Acute encephalopathy is caused by a viral infection that induces highfever, such as in people afflicted with the influenza virus.
Severe sequela frequently persists if acute encephalopathy
has progressed [1]. Thus, a system is needed to support thediagnosis of acute encephalopathy at early onset to avoid
severe sequela.
To diagnose acute encephalopathy, doctors generally employ diagnostic imaging through magnetic resonance imaging(MRI) or electroencephalography using EEG. However, MRI
scans cannot be used for the diagnosis of the early onset ofacute encephalopathy because the relevant abnormalities are
not apparent [2]. On the contrary, EEG can help discoverabnormalities relating to acute encephalopathy in case of early
onset [3]. The doctor can then administer appropriate treatment
to prevent severe sequela.
There are two representative cases of status epilepticus:
acute encephalopathy with biphasic seizures and late reduced
diffusion (AESD), and prolonged febrile seizure (FS). Although FS is easily curable, AESD in patients has a highprobability of progressing to severe sequela. Distinguishing
AESD from FS using EEG is a challenging task because
AESD waves are visually similar those representing FS. Doctors thus need adequate training and experience to correctly
distinguish the AESD waves from those of FS. An experienced
doctor can manually read EEG waves and detect abnormalities
when slow waves of long wavelengths (0.5 − 8 Hz) appear

repeatedly [4]. However, abnormal findings such as waves ofthis kind frequently appear in representations of both AESD
and FS. An inexperienced doctor can thus make an erroneous
diagnosis. In this paper, we propose a method to automaticallydetermine whether a given case of acute encephalopathy can
be classified as AESD using EEG waves. In particular, ourmethod splits frequency bands used by experienced doctors fordiagnosis. Note that we consider waves that have previously
been manually selected by a doctor such that they contain no
artifacts.
Several methods have been proposed to classify casesof encephalopathy other than AESD [5]–[10]. For example,
Chandaka et al. [5] exploited a feature vector using crosscorrelation among EEG waves to diagnose epilepsy in patients.
However, the waves of AESD exhibit different characteristics
in terms of frequency distribution from those of epilepsy.
In a preliminary experiment, we were unable to obtain highclassification performance in the diagnosis of AESD using theprevalent method [5] (the likelihood of correct diagnosis was
close to chance).
In light of the above, we propose a method to determine
whether cases of acute encephalopathy are those of AESD,
using the feature vector of a band correlation histogram
extracted from EEG waves. We design our feature vector basedon knowledge of the diagnosis, where the doctor identifies
temporal differences among waves over a short period. Our
method extracts the feature by applying a bandpass filter,computing the coefficients of correlation among waves overa short period, and generating a correlation histogram basedon their distribution.
II. R ELATED WORKThe relevant methods extract features from EEG waves toclassify various types of encephalopathy. Bajaj and Pachori [6]proposed a method that uses the bandwidth of the amplitude
and phase of EEG waves to identify cases of epilepsy. Kumar
and Dutt [7] proposed a method that employs graph structures between channels of EEG to determine schizophrenia
in patients. Temko et al. [8] proposed a method using EEG
waves and the heart rate to identify cases of hypoxic-ischemic
encephalopathy, and Ahmed et al. [9] proposed a methodthat uses a Gaussian mixtures’ model to estimate the level ofsequela in patients of the same illness. Furthermore, Ahmed
et al. [10] proposed a method that uses features of speechFront head

Select an epoch (the doctor does this): Channel (1, ...,10)
FP1: Electrode (FP1, ..., T4 )Apply a bandpass filter
5

1: FP1-C3
2: C3-O1
3: FP2-C4
4: C4-O2
5: FP1-T3

6: T3-O1
7: FP2-T4
8: T4-O2
9: C3-T3
10: C4-T4

FP2

1

3

7

C3 C4
9

T3

6

2
O1

Randomly set a pair of wavesof short duration from the epoch

10 T4
4
8

Compute a correlation coefficientof the pair of waves

O2
Back head

Check the numberof pairs

Fig. 1. Setup of channels for measuring EEG waves.

NO

YES

Channel1Count the band correlation histogram

2
Fig. 3. Flow to generate the band correlation histogram.

3
4
10
Time
: Similar δ and θ waves
The pair of waves of short duration in each channel
Fig. 2. Overview of knowledge of the diagnosis of acute encephalopathy.

recognition to estimate the level of brain injury. These methods
were designed to extract a feature for each classificationof encephalopathy. On the contrary, our method extracts a
discriminative feature for AESD using a correlation histogram
computed from EEG readings of infant patients.
III. BAND CORRELATION HISTOGRAM FOR FEATURE
EXTRACTION

A. Overview
We design a feature vector to identify AESD based on
knowledge of the diagnosis by a doctor in a clinical setting.
To measure EEG waves from an infant patient, the doctor sets
electrodes on the patient’s head as shown in Figure 1. The
doctor then records the potential difference at each channel
by subtracting the potentials between electrodes. As describedin [11], [12], he/she diagnoses encephalopathy by observing
a pair of EEG waves over a short period. For instance, as
illustrated in Figure 2, the doctor checks whether similar δand θ waves (0.5 − 8 Hz) appear temporally. Five types ofbrain waves are of most interest in neuroscience (δ(0.5 − 4Hz), θ(4 − 8 Hz), α(8 − 13 Hz), β(13 − 30 Hz), γ (above 30Hz)) from low to high frequencies, respectively. We use δ andθ waves based on the doctor’s knowledge of the diagnosis. To
extract the feature to identify AESD using knowledge of thediagnosis, our method computes a correlation coefficient froma pair of waves over a short period randomly sampled from
EEG waves. It then generates a band correlation histogramusing the distribution of the correlation coefficients as shownin Figure 3. The details of our method are described below.
B. Algorithm to generate the band correlation histogram
The doctor measures the EEG waves of an infant patientand selects ones with no artifacts, but with the abnormalities
that feature in both AESD and FS. We call a selected wave
an epoch. To extract δ and θ waves used for diagnosis bya doctor, we simply apply a bandpass filter to an epoch to
extract the components of these waves. We define an epoch
xb,i (i ∈ {1, · · · , N } and, a band b ∈ {δ, θ, α, β}) containing
N channels as
xb,i = [x1b,i , x2b,i , · · · , xTb,i1 ]T ,

(1)

xtb,i

where
is the potential difference acquired by the i-thchannel at time t and T1 is the length of the epoch used forfeature extraction.
To compare waves over short periods in epoch xb,i , we set
the pair of waves acquired in period T2 . Our method selects S
pairs by randomly repeating the below procedure. We denotethe start time (mk , nk ) of the k-th pair by
mk ∼ U(0, T1 − T2 ) ,(2)nk ∼ U(0, T1 − T2 ) ,(3)where k ∈ {1, · · · , S}, and U() represents the discrete uniform
distribution that randomly returns the duration from zero toT1 − T2 .
However, when returning the margin part by Hamming
window, we randomly return the duration again. We definenthe pair of waves of short period xm
b,i , xb,j (j ∈ {1, · · · , N })using the start time (m, n) as
m+T2 Tm+1
m+2
xm]
b,i = [xb,i , xb,i , · · · , xb,i(4)

n+T2 Tn+2
xnb,j = [xn+1]b,j , xb,j , · · · , xb,j(5)

Having randomly selected S pairs of waves, we computea correlation coefficient for each. We define the correlationm,ncoefficient rb,i,j
relating the waves of short period xm
b,i andnxb,j as(6)

where cov() represents the covariance computed among thesewaves of short periods. The correlation coefficient uses them,nrange of values −1 ≤ ri,j
≤ 1.
To generate the feature vector for classification, we computethe distribution of the correlation coefficients. We define afeature vector: the band correlation histogram within thechannel hb . We compute hb to temporally compare the waves
at the same channel as
hb =

N
X
T
[h1b , h2b , · · · , hD
b ]

22

10

**

**

**

δ

θ

α

β

Frequency bands
Fig. 4. Classification performance of band correlation histogram whilechanging the frequency bands.

(7)

**

i=1

(8)

where card is an operator representing the number of elements
m,nm,nin a set consisting of rb,i,junder al ≤ rb,i,j< al+1 . Note thatm,nal = −1 + 2(l − 1)/D. When rb,i,j = 1, we count it in hDb .
The total number of pairs of waves of short period is N S. We
apply L1-norm normalization for ||hb || = 1.
IV. E XPERIMENTS
A. Dataset of EEG waves of AESD and FS
To evaluate the classification performance of our method,
we collected EEG data from 34 infant patients (22 males, 12
females; average age 1.7 ± 1.4 years). Seventeen patients had
AESD and the other half had FS. When measuring EEG waves,the patients were comatose or in deep sleep. The setup of thechannels and electrodes for the EEG readings is shown in
Figure 1. The number of channels was N = 10. The doctor
selected 10 epochs for each patient. The length of an epoch
was T1 = 15 s and the sampling interval was 10 ms.
B. Parameters of the band correlation histogram
We used 17-fold cross-validation. Data for two patients wereused as test samples and those for the remaining 32 as training
samples. We used the liner support vector machine [13], andcompared classification performance in terms of the methods
determining whether patients were suffering from AESD or
FS using the band correlation histogram. We conducted ourevaluations with frequency bands for b = δ, θ, α, and β waves,and number of bins D = 5, 10, and 20, and waves of periods
T2 = 0.5, 1, 2, and 3.5. We randomly sampled S = 1, 000
pairs of waves of short period.
Figure 4 shows the classification performance of the bandcorrelation histogram while changing the frequency band.

Number of correctlyclassified patients

22

where D is the number of bins in the histogram and hlb is them,nfrequency of the l-th bin. We count frequency hlb using rb,i,j
as
m,n
m,n
hlb = card{rb,i,j|al ≤ rb,i,j< al+1 }****Number of correctlyclassified patientsncov(xmb,i , xb,j )m,nrb,i,j=qmnncov(xmb,i , xb,i )cov(xb,j , xb,j )

**

10

5

10

20

Number of bins
Fig. 5. Classification performance of band correlation histograms using δ
waves while changing the number of bins D.

We calculated the average and the standard deviation of thenumber of patients correctly classified by fixing a certain
parameter and changing the others. We used Bonferroni’s
method for multiple tests. We also used the Wilcoxon signedrank test (p < 0.01 : ∗∗). We can see that δ waves yielded
better performance than θ,α and β waves. Figure 5 shows
the classification performance of the methods using the bandcorrelation histogram using δ waves while changing the number of bins D and Figure 6 shows classification performanceusing δ waves while changing the duration of waves. We cansee that D = 10 yielded better performance than D = 20, andT2 = 1 yielded better performance than T2 = 0.5, 2, and 3.5.
We obtained the best performance (21 patients were correctlyclassified) using the correlation histogram of θ waves with
D = 10 and T2 = 2, or D = 20 and T2 = 3.5.
C. Comparison with methods that spatially compute the correlation coefficient
We compared the proposed method with an inter-channel
method that spatially compares waves at the same time, and arandom method that spatially compares them at different times.
The proposed method used feature vector hδ with D = 10 and

**
**

**

**

**

**

Number of correctlyclassified patients

22

10

0.5

1

2

3.5

Duration of waves
Fig. 6. Classification performance of band correlation histograms using δ
waves while changing the duration of waves.

****

Number of correctlyclassified patients

22

10
Ours

Inter-channel

RandomMethods used to compute the correlation coefficientFig. 7. Classification performance of the band correlation histogram using thethree methods on δ waves. The inter-channel method spatially compares waves
at the same time. The random method spatially compares them at different
times.

T2 = 1. All three methods used the linear SVM classifier. We
used the same dataset as described in Section IV-B. Figure 7
shows the classification performance of band correlation histograms using the three methods on δ waves. We see that the
proposed method yielded better performance than the interchannel method and the random method. As described in
Section III-A, an expert doctor checked to whether similar δ
waves (0.5−4 Hz) appear temporally. We think that the feature
vector of the proposed method generated by comparing wavesof short periods benefited from knowledge of the diagnosis of
AESD.
D. Comparison with prevalent method
We compared the proposed method with a method prevalent
in practice [5] using the five elements of the cross-correlation(peak value, instant at which peak occurs, centroid, equivalent
width, mean square abscissa) computed from the EEG waves
between channels. The prevalent method used a feature vectorof 5 × 45 = 225 dimensions by computing the five elements
between 10 C2 = 45 combinations of channels. Our method

used the feature vector described in Section IV-C. Bothmethods used a linear SVM classifier. We used the samedataset as described in Section IV-B. Our method correctlyclassified 20 patients whereas the prevalent method classified
only 18 correctly. Thus, our method improved classification
performance compared with the prevalent method. We thus
think that it can help correctly diagnose AESD. The prevalentmethod directly applied cross-correlation to the EEG waves.
On the contrary, our method applied correlation to waves ofshort period selected from the EEG waves. An expert doctor
cannot observe the EEG waves all at once. We think that theband correlation histogram generated by waves of short period
benefited from knowledge of the diagnosis of AESD.
E. Visualization
We investigated the bins of the band correlation histogramthat contributed to enhancing the classification performanceof AESD. We used the parameters of the feature describedin Section IV-C. Figures 8 (a), (b), and (c) show the bandcorrelation histograms using δ waves for AESD and (d),(e), and (f) show those for FS. As shown in Figure 8, thefrequencies of the bins for AESD representing the correlation
coefficients −1 or 1 were higher than those for zero in theδ waves. Figures 9 (a) and (b) show the weights using thenormal vector to the hyperplane of the linear SVM classifier.
The positive weights were discriminative in classifying AESD
and the negative weights in classifying FS. We see that theweights −1 and 1 took large positive values in δ waves. We
think that the pairs of waves taking −1 or 1 as coefficientscontained discriminative features in δ waves.
F. Evaluation using a control group
We evaluated classification performance on a control groupof healthy infants and another consisting of those sufferingfrom encephalopathy. The former consisted of patients who
had been cured of FS and shuddering attacks, or were originally healthy. The other group contained infants suffering fromepilepsy (ES). We added 18 healthy infants (10 males, eightfemales; average age, 8.4 ± 2.3 years), 11 cured infants (sixmales, five females; average age, 3.3±2.9 years), and six otherencephalopathy patients (three males, three females; averageage, 2.8 ± 2.0 years) to the dataset described in IV-A. The
doctor measured the EEG waves using the same setup as in
Figure 1. The EEG waves of the added infants did not yieldabnormal findings. The doctor selected an epoch containing
normal, slow waves of sleep. We used data for 52 patientsin total: data for 34 patients (17 AESD patients, nine healthy
patients, six cured infants, and two ES patients) as the training
sample and those for 18 (nine healthy patients, five cured
infants, and four ES patients) as test sample. We generated
feature vectors and a classifier in the manner described in
Section IV-C. We calculated the average and standard deviation of the number of patients correctly classified by changing
the epochs selected. Iterated 10 times, our method correctlyclassified 14.0±0.0 test patients. We thus think that our featureFrequency

0.18

-1

0

Correlation coefficient
(a)

0

1

0

Correlation coefficient
(b)

-1

0

Correlation coefficient

1

-1

0

1

0

1

Correlation coefficient
(c)

0.18

Frequency
0

0

1

0.18

Frequency

0.18

-1

Frequency

0Frequency

0.18

Frequency

0.18

0

-1

0

Correlation coefficient

(d)

1

0

-1

Correlation coefficient

(e)

(f)

Fig. 8. Examples of correlation histograms of δ waves of AESD in (a), (b), (c) and FS in (d), (e), (f).

Weight

0.14

-0.14

-1

0
Correlation coefficient

1

Fig. 9. SVM weights contributing to classifying AESD and FS using δ waves.

can help identify patients with AESD with the likelihood ofprogressing to severe sequela.
V. C ONCLUSION
In this study, we proposed a method for identifying AESD
among infants suffering from acute encephalopathy using aband correlation histogram. Our method computes the distribution of the correlation coefficients of EEG waves takenover a short period in frequency bands based on the doctor’s knowledge of the diagnosis. We also showed that our
method can improve classification performance compared witha prevalent method by conducting evaluations on a dataset of
infant patients with acute encephalopathy.
In future work, we intend to evaluate classification performance with a greater number of the patients and conduct a user
study involving doctors who are not experienced in treating
AESD.
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