文献类型：会议论文

英文题名：Epileptic seizure detection based on spectral features and extreme learning machine

作者：Chen, Lanlan[1]; Zou, Junzhong[1]; Zhang, Jian[1]

机构：[1] Department of Automation, School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China

会议日期：October 18, 2013 - October 21, 2013

会议地点：No. 130, Meilong Road, Shanghai, China

语种：英文

外文关键词：Knowledge acquisition - Feature extraction - Neurophysiology - Diagnosis - Learning systems

摘要：Since epileptic seizures occur irregularly and unpredictably, automatic seizure detection in EEG recording is significant in the diagnosis of patients with epilepsy. This paper proposed a new epileptic EEG classification approach based on spectral features and extreme learning machine. The spectral power in each 2 Hz width sub-band was measured. Then the principal spectral features were fed to the extreme learning machine (ELM) to discriminate between seizure and non-seizure EEG epochs. The experiments demonstrate that the proposed system can achieve satisfying detection accuracy with low cost of false detection. ELM classifier can reach faster learning speed than conventional learning algorithm.