1. Classification of Pre-ictal and Interictal Periods Based
on EEG Frequency Features in Epilepsy
Bharat K Karumuri1 *
, Ioannis Vlachos2
, Rui Liu2
, Joshua Adkinson2
, Leonidas Iasemidis1
1
Biomedical Engineering, Louisiana Tech University, Ruston, LA 71272, USA
2
Department of Mathematics, Louisiana Tech University, Ruston, LA 71272, USA
*
Corresponding author: bkk005@latech.edu
Abstract – A seizure prediction system has the potential to
significantly help patients with epilepsy. For a seizure forecasting
system to work effectively, computational algorithms must reliably
identify periods with high probability of seizure occurrence. We
herein report results of a classification approach based on machine
learning of EEG features in the frequency domain and aimed at
differentiating between pre-ictal (close to seizure onsets) and
interictal (far away from seizures onset) periods in long-term
intracranial EEG recordings from the brain of 5 epileptic dogs.
Evaluation of performance by the area under the ROC curve ranged
from 0.84 to 0.96 in four dogs, while for the fifth dog was
considerably less (0.55), resulting to a global value of 0.87 across
dogs. These results offer supporting evidence that seizures may be
predictable with a proper analysis of the EEG.
I. INTRODUCTION
Epilepsy affects 1% to 2% of the world population and is
characterized by occurrence of spontaneous seizures, that is,
temporary disturbances of ongoing electrical activity of the
brain [1]. For many epilepsy patients, treatment with anti-
epileptic drugs (AEDs) prevents seizures but is typically
accompanied with side effects. Furthermore, in 20% to 40% of
patients AEDs are not effective and patients continue to
experience spontaneous seizures [2]. A seizure prediction
system bundled with timely intervention has the potential to
improve treatment of epilepsy via closed-loop
neuromodulation.
We employed traditional signal analysis methods to estimate
from segments of multichannel electroencephalographic (EEG)
recordings values of coherence between brain sites and spectral
entropy at individual sites. Subsequently we used linear
discriminant analysis to classify the EEG segments as
temporally close to (pre-ictal states) or far away from
(interictal states) seizures (ictal states) in terms of their
determined pre-ictality scores.
II. METHODOLOGY
A. Data
The long-term EEG data were obtained from the American
Epilepsy Society Seizure Prediction Challenge database
(https://www.kaggle.com/c/seizure-prediction/data). EEGs
were acquired from electrodes placed inside the brain
(intracranial EEG – iEEG) using a 16 channel ambulatory EEG
recording system. (Dogs numbered 1 to 4 had all 16 channels
of data available, while Dog 5 had only 15.) The sampling
frequency was 400 Hz. For each animal, 2 sets of data are
provided: pre-ictal periods (tens of minutes in duration) one
hour prior to seizures onset excluding the five minute periods
immediately preceding the seizure, and interictal periods
(hours in duration) from at least one week away from seizures.
Pre-ictal and interictal periods were provided in the form of 10
min consecutive epochs of data for each animal.
B. Measures
Two traditional measures of the EEG signal characteristics
in the frequency domain are the coherence function and
spectral entropy. Coherence determines the strength of the
linear association between two signals at particular
frequencies. Spectral entropy is used to measure the disorder or
uncertainty hidden in the spectrum of a signal.
1) Coherence (Coh): Coherence is a quantitative measure of
the linear dependency between two signals, in our case EEG
signals recorded from two sites in the brain. High coherence
between EEG signals recorded at different sites implies high
connectivity between these sites. The coherence function is
obtained by normalization of the cross-power spectrum by the
signals’ auto-power spectra and its magnitude is used to
measure the strength of the interaction between signal, while
its phase denotes their phase difference at certain frequencies
[3].
For two signals ‫ݔ‬ሺ‫ݐ‬ሻ and ‫ݕ‬ሺ‫ݐ‬ሻ, with cross-power spectrum
‫ܥ‬௫௬ሺ݂ሻ and corresponding auto-power spectra ‫ܥ‬௫௫ሺ݂ሻ
and ‫ܥ‬௬௬ሺ݂ሻ, the magnitude squared coherence function ሺȞሻ at a
frequency ݂ is given by
Ȟ௫௬
ଶ ሺ݂ሻ ൌ
ห‫ܥ‬௫௬ሺ݂ሻห
ଶ
‫ܥ‬௫௫ሺ݂ሻ‫ܥ‬௬௬ሺ݂ሻ
Ǥ
Coherence values are in the range of 0 to 1, where 0 at a
frequency f means that the corresponding components of the
signals of frequency f are uncorrelated, and 1 means that they
are fully correlated with a possible constant difference in
amplitude and phase shift. In this study, we estimated the
average of coherences over the full frequency band (0.5 – 50
Hz) for each pair of channels.
2) Spectral Entropy ሺܵ‫ܧ‬ሻ: Spectral entropy measures the
complexity of a signal by considering the amplitudes of the
power spectrum at different frequencies normalized by the
total power of the signal as probabilities, and then calculating
Shannon’s entropy [4]. It attains maximum value when the
spectrum is flat. ܵ‫ܧ‬ is given by
ܵ‫ܧ‬ ൌ െ
σ ܲ௙ Ž‘‰ ܲ௙
Ž‘‰ሺܰሻ
ǡ
2016 32nd Southern Biomedical Engineering Conference
978-1-5090-2133-8/16 $31.00 © 2016 IEEE
DOI 10.1109/SBEC.2016.9
9

2. where ܲ௙ are normalized spectral amplitudes at frequency ݂
and ܰ is the number of discrete frequencies at which power
spectral density (PSD) is estimated. In this study, SE was
estimated for the following 8 different EEG frequency bands
(0.5-50 Hz; 0.5-4 Hz; 4-7 Hz; 8-15 Hz; 16-31 Hz; 32-50 Hz;
0.8-34 Hz; 0.8-47 Hz ) per channel.
C. Classification
Linear discriminant analysis (LDA) was used to classify
EEG data segments as interictal or pre-ictal [5]. The LDA
based classification was performed per segment under a leave-
one-hour-out cross validation scheme. The average values of
coherence over the full spectrum per pair of available channels
and the individual values of the spectral entropy per EEG
frequency band per channel were used in this study. Coherence
values across all pairs of electrodes and the SE values across
all electrodes per segment (total of 248 for Dogs 1-4 and 235
for Dog 5) were then input as features to the LDA to produce a
binary output (0 if classified interictal and 1 if classified pre-
ictal) as class value for each segment.
III. RESULTS
The EEG data in an epoch were further divided into smaller
10sec segments (60 for each epoch). Preprocessing of each 10
sec EEG segment by filtering techniques was employed: a
notch filter to remove the 60 Hz line. Post-filtering the
frequency-based features of coherence and spectral entropy
were evaluated per segment. Since the number of interictal
segments was much larger compared to that of the pre-ictal
segments (interictal periods are much longer than pre-ictal
periods), we used the Synthetic Minority Oversampling
Technique (SMOTE) [6] to balance the two sets of data so that
the subsequent statistics in the evaluation of the performance
of our classification algorithm are more meaningful. A
majority voting approach was then used to derive a score of
pre-ictality for every epoch of EEG data based on the LDA
classification outputs for the segments included in this epoch.
The Receiver Operating Characteristic (ROC) curve was then
constructed and the area under the curve (AUC) was calculated
per animal as a metric of the classifier performance.
Additionally the error rate, sensitivity and specificity were
estimated based on the pre-ictality score of each epoch and
applying a binary classification threshold of 0.5 to determine if
the epoch is pre-ictal or not.
Table I shows the AUC values for each dog obtained from
this analysis. The global AUC value across all dogs is also
provided. The AUC ranged from 0.842 to 0.961, for four dogs,
while for the fifth dog was considerably less (0.559).
Combination of the two measures gives the highest global
AUC of 0.872, almost 5% higher than using only coherence
(AUC of 0.832) or spectral entropy (AUC of 0.819). Table II
shows a more detailed analysis of the classification results
from the combination of measures, including the values for the
error rate, sensitivity and specificity. The obtained error rate
ranged from 5.7% to 18.1%, sensitivity from 37.5% to 83.3%,
and specificity from 84.2% to 96.1%.
IV. DISCUSSION
Classification of interictal and pre-ictal epochs was better
achieved by combination of the coherence and spectral entropy
features. The high specificity (0.914) indicates good
performance of the algorithm with a small number of false
positives, but the relatively small sensitivity (0.649) indicates a
significant number of missed pre-ictal epochs. The low values
of sensitivity is attributed mostly to one of the 5 animals (Dog
1, sensitivity value of 0.375). This dog had the smallest
duration of the preictal data which, in conjunction with the
large variability of the precictal states of the brain, could
highly affect the performance of the LDA classifier. Finally,
the obtained AUC values using both coherence and spectral
entropy as features is an indication that combination of features
could improve seizure prediction. In summary, these
preliminary results point to the possibility of seizure prediction
by appropriate analysis of the EEG.
TABLE I: AREA UNDER ROC FOR EACH DOG USING EITHER
COHERENCE, SPECTRAL ENTROPY OR BOTH
AUC
Coh SE (Coh, SE)
Dog 1 0.500 0.471 0.559
Dog 2 0.951 0.866 0.955
Dog 3 0.800 0.838 0.859
Dog 4 0.813 0.835 0.849
Dog 5 0.920 0.850 0.965
Global 0.832 0.819 0.872
TABLE II: CLASSIFICATION CHARACTERISTICS FOR EACH DOG USING
BOTH THE FEATURES OF COHERENCE AND SPECTRAL ENTROPY
Error rate
(1-accuracy)
Sensitivity Specificity
Dog 1 0.181 0.375 0.842
Dog 2 0.102 0.833 0.904
Dog 3 0.057 0.583 0.961
Dog 4 0.164 0.639 0.860
Dog 5 0.058 0.800 0.951
Global 0.104 0.649 0.914
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