H. Va va di et al. / J. Biomedical Science and Engineering 3 (2010) 1182-1189

1188

sub-bands have separation rate over 99.99%. distin-

guishment for this pair of group in beta sub-band is 54.

99% and for delta sub-band is 89.08%.

Comparing O with S shows up to 99.99% separation

probability for EEG, gamma, beta, delta and theta sub-

bands and 99.98% for alpha subband.

For groups F and N the EEG signal has separation

with over 99.99% probability and 99.98% for gamma

sub-band. For alpha sub-band we can’t tabulate P-value

since both of the t-score and degree of freedom were

zero. Beta and theta sub-bands don’t show good separa-

tion rate (41.17% and 0.269%) but delta sub-band has

99.57% probability for separation rate.

For comparing interictal and ictal states (F and S, and

N and S) EEG signal and alpha, beta and theta sub- band s

have separation probability ov er 99.99 % for both pa irs F,

S and N, S. As we see in table 3 separation probability

between F and S for gamma sub-band is 96.47% and

86.29% for delta sub-band.

Separation rate for pair N, S in gamma sub-band isn’t

good not at all (1.84%) but for alpha sub-band is suitable

(99.13%).

4. DISCUSSION

Some other studies have been done in feature extraction

for epilepsy detection. But most of them just have used

for separate 3 groups of these 5 groups and have ignored

the other ones [3,23]. The extracted parameters in this

study can separate all of these 5 groups. Considering just

3 groups of these 5 groups shows significant difference

(see Figure 5). In comparison ApEn with CD and LLE

[3] we can see two major improvements.

1) We calculate separation rates for all 10 pairs with

all of 5 groups but in [3] and some other studies just 3

groups have been considered.

2) ApEn values can separate most sub-bands of each

pair but as we see in Tab l e 4 . In some sub-bands corre-

lation dimension or largest lyapanov exponent don't

show significant difference. And in other sub-bands

these values just can separate 2 or 3 pair of groups.

5. Conclusion

In this study, the Approximate Entropy combined with

wavelet analysis used to extract the features for epilepsy

detection. In order to automatic detection of epileptic

activity in EEG signals we have 3 different states

(healthy, interictal and ictal) and significant results are

obtained. The value of ApEn can be used to distinguish

the different EEG state. According to ApEn analysis

features of EEG and their sub-bands show acceptable

performances in our approach. Our extracted feature can

be useful and applicable for automatic detection of brain

diseases such as epilepsy. The approaches of using ApEn

combined with wavelet analysis suggest new idea and

method for detecting the features of epileptic activities in

EEG signal.

This method also can be used for other non-stationary

signals and other approach. Because the speed of this

method is high enough, we can use this method for

real-time non-stationary signals.

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