| Title | Epileptic electroencephalogram signal classification based on sparse representation |
| Authors | Wang, Jing Guo, Ping |
| Affiliation | Laboratory of Image Processing and Pattern Recognition, Beijing Normal University, Beijing, 100875, China Peking University Health Science Center, School of Foundational Education, Xueyuan Rd., Beijing, China |
| Issue Date | 2011 |
| Citation | International Conference on Neural Computation Theory and Applications, NCTA 2011.Paris, France. |
| Abstract | Epilepsy seizure detection in Electroencephalogram (EEG) is a major issue in the diagnosis of epilepsy and it can be considered as a classification problem. According to the particular property of EEG, a novel method based on sparse representation is proposed for epilepsy detection in this paper. Classification accuracy, robustness on noisy data and parameters (the size of dictionary and the number of features) of proposed method are tested and analysed on the public available data. The proposed method can obtain the highest classification accuracy among the discussed methods when the suitable parameters are set, and the proposed method based on sparse representations for classification is robust to noise. This is consistent with the theory that sparse representations can capture the inherent structure of signal. Furthermore, it is shown by experiments that the optimal selection of the parameters is critical to the performance of epilepsy detection. |
| URI | http://hdl.handle.net/20.500.11897/412907 |
| Indexed | EI |
| Appears in Collections: | 医学部待认领 |
