A real-time system that provides detection and detection and identification of epilepsy

(Press-News.org) The automatic detection and identification of electroencephalogram waves play an important role in the prediction, diagnosis and treatment of epileptic seizures. Unfortunately, in previous experiments, training data and test data from electroencephalogram signals are often derived from the same cases, which may affect the clinical applicability of the classifiers. Zhen Zhang and colleagues from Zhongshan School of Medicine, Sun Yat-sen University combined a nonlinear dynamics index–approximate entropy with a support vector machine that has strong generalization ability to classify electroencephalogram signals at epileptic interictal and ictal periods. The researchers also verified 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. Their findings indicate that a nonlinear dynamics index trained classifier can effectively identify epileptic electroencephalogram signals, and has good generalization ability. This combined system is simple and fast running, which has a certain significance for the development of clinical real-time systems for detection and identification of epilepsy, and creation of a new diagnosis and treatment system of epilepsy. These results are published in the Neural Regeneration Research (Vol. 8, No. 20, 2013).

### Article: " Approximate entropy and support vector machines for electroencephalogram signal classification," by Zhen Zhang1, Yi Zhou1, Ziyi Chen2, Xianghua Tian3, Shouhong Du3, Ruimei Huang1 (1 Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China; 2 Department of Neurology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, Guangdong Province, China; 3 College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, Xinjiang Uygur Autonomous Region, China)

Zhang Z, Zhou Y, Chen ZY, Tian XH, Du SH, Huang RM. Approximate entropy and support vector machines for electroencephalogram signal classification. Neural Regen Res. 2013;8(20):1844-1852.

Contact: Meng Zhao
eic@nrren.org
86-138-049-98773
Neural Regeneration Research
http://www.nrronline.org/ Full text: http://www.sjzsyj.org:8080/Jweb_sjzs/CN/article/downloadArticleFile.do?attachType=PDF&id=655