Background: Epilepsy is one among the neurological disorders, which occurs due to
temporary and irregular electrical disturbance in the brain. EEG signals are recorded from the patients
with seizure. Gaussian filter is one of the pre-processing approaches, which is mainly applied
to remove the unwanted signal and improve the signal quality. Harris operator is utilized for key
point localization to remove the bad key points from the pre-processed signal. Here, the Scale Invariant
Feature Transform (SIFT) is employed with the Local Binary Pattern (LBP) to identify the key
points of the signal and these signal values are considered as the feature vector values, which are applied
on Multiclass least square Support Vector Machine (MLS-SVM) with four different coding
schemes: Error correcting code output code of MLS-SVM (MLS-SVM_ECOC), minimum output
codes of MLS-SVM (MLS-SVM_MOC), one versus one of MLS-SVM (MLS-SVM_1v1), one versus
all of ML-SVM (MLS-SVM_1vA) using kernel function to categorize epileptic seizure in Multiclass
EEG signals by comparing the test features with trained features. The attained results demonstrated
that the proposed technique outperformed others existing methods.
Methods: The noise is present in the real-time EEG signals. The noise is removed by applying
Gaussian filter to the input signal. Key point is localized to generate the features by using the algorithm
of SIFT. Then the local features are obtained by the method of LBP. Finally, the signals are
categorized into a seizure and seizure-free signal by comparing the optimized features with trained
features in SVM classifier. In this work, a novel classification algorithm i.e., Multiclass least square
Support Vector Machine (MLS-SVM) is introduced with four coding schemes: MLS-SVM_ECOC,
MLS-SVM_MOC, MLS-SVM_1v1, and MLS-SVM_1vA for multiclass classification of EEG signal.
To solve the multiclass categorization in EEG signal, we reformulate the extracted features from
the EEG signal to the set of binary classification by utilizing four different output coding schemes.
The proposed technique is assessed using the performance metrics like accuracy and compared with
other previously existing methods.
Results: The performance of the proposed technique is evaluated in terms of accuracy. In the suggested
classifiers, we select 5-fold cross-validation. At every time, one fold is kept for testing set and
remaining four folds are utilized for training set. The result shows that γ= 1 and σ2=10 is the best optimal
parameter combination that produces the highest average classification accuracy 99.22% for
MLS-SVM_1v1 as compared to the rest of the three proposed output scheme codings.
Conclusion: Epilepsy is the neurological disorder where the electrical movement of the brain is
changed because of the person’s activity changes from the up-normal level. The symptoms of the
seizure are unconsciousness and health issues. It may disturb the complete body or parts of the brain.
During evaluation, distinct experimental measures are used to analyze the performance of the classifier.
The accuracy of 99.29 % was reported by using the proposed method. From the results, it is validated
that the anticipated MLS-SVM_1v1 offers the better results.