Epileptic EEG Detection Using a Multi-view Fuzzy Clustering Algorithm with Multi-medoid

Qianyi Zhan, Yizhang Jiang, Kaijian Xia, Jing Xue, Wei Hu, Xinghuang Lin, Yuan Liu
2019 IEEE Access  
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
more » ... cribes the cluster structure by assigning representative weights to each sample in the cluster. Considering that the multiview 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. INDEX TERMS Epileptic EEG, multi-view, multi-medoid, fuzzy clustering. KAIJIAN XIA received the M.S. degree from Jiangnan University, Wuxi, China, in 2010. He is currently pursuing the Ph.D. degree with the China University of Mining and Technology. His research direction is medical information and medical image proceeding. He is currently with the Department of Computer, Changshu No. 1 People's Hospital. He has published several articles in international journals, including Journal of medical systems and Journal of medical imaging and health informatics.
doi:10.1109/access.2019.2947689 fatcat:xni2nfru4nfbxkflssq6ntt7xa