(1) The characteristic index of the electroencephalogram signal was extracted using a nonlinear dynamics method, and a support vector machine was used for the classification of epileptic electroencephalogram signals. Our findings are more accurate than previous classification studies.Research Highlights
(2) Previous studies used electroencephalogram data from one or two cases in one electroencephalogram database, while we selected electroencephalogram data from four epileptic patients from the electroencephalogram database in two different hospitals, so the results are more representative.Research Highlights
(3) In this study, electroencephalogram data from different cases were regarded as training data and test data for the support vector machine, which was different from previous studies that only used data from the same case, so our results are more meaningful for the prediction of clinical seizures.Research Highlights
(4) We calculated the average classification accuracy rate of three cases as the final results, which are more convincing. Our findings indicate that a nonlinear dynamics index trained classifier can effectively identify epileptic electroencephalogram signals, and has good generalization ability.
The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. In this study, a nonlinear dynamics index-approximate entropy and a support vector machine that has strong generalization ability were applied to classify electroencephalogram signals at epileptic interictal and ictal periods. Our aim was to verify whether approximate entropy waves can be effectively applied to the automatic real-time detection of epilepsy in the electroencephalogram, and to explore its generalization ability as a classifier trained using a nonlinear dynamics index. Four patients presenting with partial epileptic seizures were included in this study. They were all diagnosed with neocortex localized epilepsy and epileptic foci were clearly observed by electroencephalogram. The electroencephalogram data form the four involved patients were segmented and the characteristic values of each segment, that is, the approximate entropy, were extracted. The support vector machine classifier was constructed with the approximate entropy extracted from one epileptic case, and then electroencephalogram waves of the other three cases were classified, reaching a 93.33% accuracy rate. Our findings suggest that the use of approximate entropy allows the automatic real-time detection of electroencephalogram data in epileptic cases. The combination of approximate entropy and support vector machines shows good generalization ability for the classification of electroencephalogram signals for epilepsy.