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Feature extraction and classification are still challenging tasks to
detect ictal (i.e., seizure period) and interictal (i.e., period between
seizures) EEG signals for the treatment and precaution of the epileptic
seizure patient due to different stimuli and brain locations. Existing
seizure and non-seizure feature extraction and classification techniques
are not good enough for the classification of ictal and interictal EEG
signals considering for their non-abruptness phenomena, inconsistency in
different brain locations, type (general/partial) of seizures, and hospital
settings. In this paper we present generic seizure detection approaches for
feature extraction of ictal and interictal signals using various
established transformations and decompositions. We extract a number of
statistical features using novel ways from high frequency coefficients of
the transformed/decomposed signals. The least square support vector machine
is applied on the features for classifications. Results demonstrate that
the proposed methods outperform the existing state-of-the-art methods in
terms of classification accuracy, sensitivity, and specificity with greater
consistence for the large size benchmark dataset in different brain
locations.
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