Prediction of epilepsy based on common spatial model algorithm and support vector machine double classification.
10.7507/1001-5515.201911042
- Author:
Yuxiao WANG
1
;
Wei JIANG
1
;
Zhi LIU
1
;
Chengxiao BAO
1
Author Information
1. School of Information Science and Engineering, Shandong University, Qingdao, Shandong 266237, P.R.China.
- Publication Type:Journal Article
- Keywords:
common spatial model;
double classification;
epilepsy electrical signal;
feature combination;
support vector machine
- MeSH:
Algorithms;
Electroencephalography;
Epilepsy/diagnosis*;
Humans;
Signal Processing, Computer-Assisted;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2021;38(1):39-46
- CountryChina
- Language:Chinese
-
Abstract:
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 ictal periods. Finally, three categories of recognition were realized. The experimental data came from the epilepsy study at the University of Bonn in Germany. The average recognition rate was 98.73% in the first category and 99.90% in the second category. The experimental results show that the introduction of spatial characteristics and double classification model can effectively solve the problem of low recognition rate between interictal and ictal periods in many literatures, and improve the identification efficiency of each period, so it provides an effective detecting means for the prediction of epilepsy.
