Epileptic EEG Detection Using a Multi-View Fuzzy Clustering Algorithm with Multi-Medoid

Abstract

Using clustering algorithms to automatically analyze EEGs of patients and to identify the characteristic waves of epilepsy is of high clinical value. Traditional clustering algorithms mostly use a calculated virtual single representative medoid point to describe the cluster structure, but this single representative medoid point has insufficient information. To accurately capture more accurate intracluster structural information, a representative multi-medoid points strategy is adopted, which describes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multi-view learning mechanism combines information from each view to improve the algorithm's clustering performance, a multi-view fuzzy clustering algorithm with multi-medoid (MvFMMdd) is proposed. This algorithm discards the approach of the traditional fuzzy clustering algorithm, which uses a single virtual representative point to characterize the cluster structure, and uses several real representative points to describe the cluster structure. Experiments verify the medical significance of the proposed algorithm.