The recognition methodology study of epileptic EEGs based on support vector machine / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 919-924, 2013.
Article
in Chinese
| WPRIM
| ID: wpr-352140
ABSTRACT
EEG recordings contain valuable physiological and pathological information in the process of seizure. The dynamic changes of brain electrical activity provide foundation and possibility for research and development of automatic detection system about epilepsy. In this paper, a nonlinear dynamic method is presented for analysis of the nonlinear dynamic characteristics of EEGs and delta, theta, alpha, and beta sub-bands of EEGs based on wavelet transform. The extracted feature is used as the input vector of a support vector machine (SVM) to construct classifiers. The results showed that the classification accuracy of SVM classifier based on nonlinear dynamic characteristics to classify the EEG into interictal EEGs and ictal EEGs reached 90% or higher. The support vector machine has good generalization in detecting the epilepsy EEG signals as a nonlinear classifier.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Signal Processing, Computer-Assisted
/
Artifacts
/
Nonlinear Dynamics
/
Diagnosis
/
Electroencephalography
/
Epilepsy
/
Support Vector Machine
/
Methods
Type of study:
Diagnostic study
/
Prognostic study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2013
Type:
Article
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