Extracting and Selecting Distinctive EEG Features for Efficient Epileptic Seizure Prediction.

This paper presents compact yet comprehensive feature representations for the electroencephalogram (EEG) signal to achieve efficient epileptic seizure prediction performance. The initial EEG feature vectors are formed by acquiring the dominant amplitude and frequency components on an epoch-by-epoch basis from the EEG signals. These extracted parameters can reveal the intrinsic EEG signal changes as well as the underlying stage transitions. To improve the efficacy of feature extraction, an elimination-based feature selection method has been applied on the initial feature vectors. This diminishes redundant and noisy points, providing each patient with a lower dimensional and independent final feature form. In this context, our study is distinguished from that of others currently prevailing. Usually, these latter approaches adopted feature extraction processes, which employed time-consuming high-dimensional parameter sets. Machine learning approaches that are considered as state of the art have been employed to build patient-specific binary classifiers that can divide the extracted feature parameters into preictal and interictal groups. Through out-of-sample evaluation on the intracranial EEG recordings provided by the publicly available Freiburg dataset, promising prediction performance has been attained. Specifically, we have achieved 98.8% sensitivity results on the 19 patients included in our experiment, where only one of 83 seizures across all patients was not predicted. To make this investigation more comprehensive, we have conducted extensive comparative studies with other recently published competing approaches, in which the advantages of our method are highlighted.