Epileptic seizure detection via logarithmic normalized functional values of singular values.

• The logarithmic normalized functional singular values are employed as the features for performing the classification. • Different classifiers are employed for performing the classification. • The performance is evaluated under the white noise environment. Electroencephalograms (EEGs) play a significant role in both the detection and the prediction of the epileptic seizures. This paper proposes to employ the logarithmic normalized functional singular values as the features for performing the classification of both the two class problem (normal and seizure set) and the three class problem (normal, seizure free and seizure set). Here, the EEGs are taken from two well known datasets. First, each EEG is decomposed via the singular spectrum analysis (SSA) to obtain the singular values. Then, the logarithmic normalized functional values of these singular values are calculated to form the feature vectors for performing the classification of the epileptic seizure. Next, different classifiers including the support vector machine (SVM), the k nearest neighbor classifier, the extreme learning machine (ELM) and the artificial neural network (ANN) are employed for performing the classification. Finally, a ten fold cross validation procedure is employed to ensure the reliability and the stability of the classifiers. The computer numerical simulation results show that the k nearest neighbor classifier achieves the best performance compared to other classifiers for performing both the two class epileptic classification and the three class epileptic classification. Our proposed method also achieves the higher classification accuracies compared to the case without performing the logarithmic normalized operation. Moreover, the performance of our proposed method is evaluated on the original EEGs under the white noise environment at different signal to noise ratios. Similar results are obtained. [ABSTRACT FROM AUTHOR]