A Novel Feature Extraction Method for Epileptic Seizure Detection Based on the Degree Centrality of Complex Network and SVM
Epilepsy is a kind of ancient disease, which is affecting the life of patients. With the increasing of incidence of epilepsy, automatic epileptic seizure detection with high performance is of great clinical significance. In order to improve the efficiency of epilepsy diagnosis, a novel feature extraction method for epileptic EEG signal based on the statistical property of the complex network and an epileptic seizure detection algorithm, which is composed of the extracted feature and support vector machine (SVM) is proposed. The EEG signal is converted to complex network by horizontal visibility graph firstly. Then the degree centrality of complex network as a novel feature is calculated. At last, the extracted feature and SVM construct automatic epileptic seizure detection. A classification experiment of the epileptic EEG dataset is performed to evaluate the performance of the proposed detection algorithm. Experimental results show the novel feature we extracted can distinguish ictal EEG from interictal EEG clearly and the proposed detection algorithm achieves high classification accuracy which can be up to 93.92 %.
KeywordsEpileptic seizure detection Feature extraction method Epileptic electroencephalograph (EEG) Degree centrality Support vector machine (SVM) Horizontal visibility graph (HVG)
This work was supported by the National Natural Science Foundation of China (Grant No. 61201428, 61302128), the Natural Science Foundation of Shandong Province, China (Grant No. ZR2010FQ020, ZR2013FL002), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003, BS2014DX015).