Detection of epileptical seizures based on alpha band statistical features

Significant research has been going in the field of automated epileptical seizure detection using Electroencephalogram (EEG) data. The EEG signal consists of different frequency bands, which correspond to the different emotional and mental activities of the humans. Most of the research work uses the whole frequency spectrum for the detection of seizures. In this paper, first time the proposed automated system utilizing machine learning technique using only alpha band (8–12 Hz). This paper uses Short-time Fourier transform (STFT) due to its high speed and less complexity in hardware implementation to convert EEG data in time–frequency (t–f) plane. As brain oscillations of a person vary in different health conditions, four statistical features have been extracted from t–f plane of alpha band. The detection performance of the features of alpha band has been analyzed on six classifiers using tenfold cross-validation which shows that the Random Forest (RF) classifier gives the best performance among different classifiers for most of the experiments performed. This study has achieved the best classification accuracy of 98% and ROC analysis revealed maximum Area Under Curve (AUC) of 1 to distinguish the seizures and healthy. Hence, the statistical features of the alpha band depict to be a potential biomarker for the real time detection system.