基于排列熵和支持向量机的癫痫发作预测研究

iming at the great harm caused by epileptic seizures for patients and leave enough spare time for clinical treatment, the study put forword a system which can predict the seizure in advance for people with epileptic. This method based on 21 epileptic patients and extracted permutation entropy as a feature vector which has lower algorithm complexity. Then the vector was input into the support vector machine (SVM) to train a learning model and identify the ictal samples. Taking full account of patient differences, it used voting mechanism to determine the patient's state. Finally, the method realized a real-time prediction for epileptic. The results show that this method can predict 81% of the seizures with more than 50 minutes before the onset of epilepsy, and it has a low false alarm rate. The method provides a solid foundation for theoretical research of seizure prediction system.