S. K. XIE ET AL.
Copyright © 2013 SciRes. ENG
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. 2) Calculate for each Fourier
frequency
for each test signal
j
of group
l
the sample
mean of
, denoted by
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with respect to all the training signals
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of the group
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. 3) Calculate the sample variance
of
with respect to
. 4) A test signal
j
is classi-
fiedinto a group
l
if
is the smallest value of
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2.3. Experimental Data
We consider the publicly available data [ 7] that h ave five
sets, denoted as Set A, B, C, D, and E, respectively. Sets
A and B consist of the data segments taken from the sur-
face of EEG recordings of five healthy volunteers. Data
in Sets C, D and E come from patients suffering from
epilepsy. Set E contains only seizure activity. Each data
set (i.e., from A to E) contains 100 single-channel EEG
signals, each with a total of 4097 sample points. The
classification of the normal type and the type with epi-
leptic seizure activities has been widely studied for the
considered data sets (i.e., Sets A, B, C, D and E) and a
high accuracy of these classifications have been achieved.
However, in this paper, we focus on the multi-class clas-
sification problem.
3. Results
3.1. Two-Class Classificat ion
Before we address the three-class classification problem
(i.e., the classification problem of the normal, inter-ictal
and seizure classes), first, we split randomly the data of
each of the Sets A, B, C, D and E, respectively, into the
training data set and the test data set of 50 signals each.
Next, we study if the test data of each data Set A, B, C, D,
and E can be successfully classified as of the type of the
respective training data set using the statistical similarity
test. To carry out this test, the confidence band of each
average of the Fourier power sp ectra of the pre-processed
training data set of each Set A, B, C, D and E is calcu-
lated. Next, the computed statistical similarity value of
CI test is compared to a pre-defined statistical similarity
level to enable a classification decision of each test signal.
If the computed statistical similarity value is higher than
the pre-defined level, then the pre-processed test signal is
classified as of the type of the respective training data.
Finally, we count the total number of the correct classi-
fications. Table 1 shows the results of the accuracy of
the two-class classification problem when different levels
of the statistical similarity tests are considered for each
pre-processed data set. For the statistical similarity level
of 0.8 we obtain an accuracy o f classification of 50 out of
50 test signals selected from Set A and an accuracy
Table 1. The number of correct classifications (displayed in
the right columns) out of 50 EEG test signals with respect to
different pre-defined statistical similarity levels (listed in
the left column) for 5 different data sets (i.e., sets A, B, C, D
and E) for two-class classification problem.
Statistical
similarity level Set A Set B Set C Set D Set E
0.95 33 12 34 40 37
0.90 45 19 41 44 46
0.85 48 29 41 45 49
0.80 50 37 41 45 49
of classification of 49 ou t of 50 test signals se lected from
Set E. However, the results in Table 1 show that the ap-
plied CIC method with statistical similarity level of 0.8
does not successfully classify the test data selected from
Set B into a group of the respective training data set.
3.2. Three-Class Classification
For the three-class classification problem, first, we split
randomly the data of each of the Sets A, B, C, D and E,
respectively, into the training data set and the test data set
of 50 signal s each. Inste ad of only co ns ideri ng t he data of
Sets A and E separa tely and ignoring the data of the Sets
C, B and D, we combine together data from different sets
(e.g., from Set C and Set D). The above described CI
based classification method and the proposed EC method
need to be modified in order to be applied to the above
three-class classification problem. The modifications of
the classification methods are needed because we do not
classify the test signals into all three possible groups. A
test signal from the normal group is classified as either a
normal or an inter-Ictal signal and a test signal from the
inter-Ictal group is classified as either a normal, or an
inter-Ictal, or an Ictal signal. The results of accuracy of
the three-classs classification, based on the CIC method
and the EC method, are reported in Table 2 and Figu re 1.
The three-class classification achieves 100% accuracy
when the proposed EC method, using only a few PCs
(i.e., 4 or 5), is applied to the training and test signals.
The CIC method used in three-class classification suc-
cessfully classifies the test data of the inter-Ictal group
and the seizure group into the inter-Ictal group and the
Ictal group, respectively, but it does not classify suc-
cessfully test data of the normal group.
Our study shows that the accuracy of classification of
the normal group test data depend on the selected feature
dimensions for both classification methods. When more
features are retained, the classification accuracy of the
normal group is decreased regardless which method is
used, i.e . the CIC method or the EC method (see Figure
1). However, for our three-class classification problem,
the EC method is more robust than the CIC method in