in the literature. In a study of drowsiness detection [36], authors extracted
Petrosian and Higuchi fractal dimensions from EEG time domain signals. Similarly
in another work, Acharya et al. [37] extracted Katz fractal dimension with other
features for the classification of various sleep stages. We have also extracted one of
the newly introduced feature, namely exponential energy by Fasil and Rajesh [26].
Some of the statistical features (mean, standard deviation and energy) are also
tested in this work.
5. Experiments and results
In this work, seizure EEG signals and non-seizure EEG signals are classified by
decomposing the EEG signal into IMFs using empirical mode decomposition. The
frequencies beyond 60 Hz are irrelevant in the EEG analysis due to the nonavailability of proper information in higher frequencies [34]. A sixth order butterworth filter is used to remove frequencies beyond 60 Hz. The signals are further
segmented into 10 non-overlapping segments. Empirical mode decomposition is
applied on the segmented EEG signals and first four IMFs are obtained. Feature are
extracted from four IMFs and averaged across the segments. Support vector
Figure 1.
Block diagram of the proposed seizure classification method.
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Advances in Neural Signal Processing