A method for detecting high-frequency oscillations using semi-supervised k-means and mean shift clustering

Abstract

This paper proposes a method to detect the high-frequency oscillations (HFOs) in epileptic seizure onset zones (SOZs) localization using semi-supervised k-means and mean shift algorithm. Wavelet entropy (WE) and teager energy operator (TEO) are adopted to distinguish HFOs from normal electroencephalogram (EEG). Labeled data are used to initialize the clustering center of semi-supervised k-means algorithm, and unlabeled data are employed to obtain physiological and suspected pathological HFOs. For the suspected pathological HFOs, the mean shift algorithm is used for clustering, and the results are analyzed by the spectral center algorithm to locate SOZs. By comparing the EEG data of five patients with the results of the other three methods, it can be seen that the method proposed in this paper has good sensitivity and specificity, which is helpful for accurate localization before clinical epilepsy surgery.