Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification

• Published in: Sheng wu yi xue gong cheng xue za zhi Vol. 38; no. 1; p. 39
• Main Authors: Wang, Yuxiao, Jiang, Wei, Liu, Zhi, Bao, Chengxiao
• Format: Journal Article
• Language: Chinese
• Published: China Sichuan Society for Biomedical Engineering 25.02.2021
• Subjects: Algorithms; Classification; Dynamic characteristics; EEG; Electroencephalography; Entropy; Epilepsy; Epilepsy - diagnosis; Feature extraction; Humans; Model testing; Nonlinear dynamics; Predictions; Recognition; Signal Processing, Computer-Assisted; Support Vector Machine; Support vector machines; support vector machine; common spatial model; double classification; epilepsy electrical signal; feature combination
• ISSN: 1001-5515
• DOI: 10.7507/1001-5515.201911042

At present the prediction method of epilepsy patients is very time-consuming and vulnerable to subjective factors, so this paper presented an automatic recognition method of epilepsy electroencephalogram (EEG) based on common spatial model (CSP) and support vector machine (SVM). In this method, the CSP algorithm for extracting spatial characteristics was applied to the detection of epileptic EEG signals. However, the algorithm did not consider the nonlinear dynamic characteristics of the signals and ignored the time-frequency information, so the complementary characteristics of standard deviation, entropy and wavelet packet energy were selected for the combination in the feature extraction stage. The classification process adopted a new double classification model based on SVM. First, the normal, interictal and ictal periods were divided into normal and paroxysmal periods (including interictal and ictal periods), and then the samples belonging to the paroxysmal periods were classified into interictal and icta