基于排列熵和支持向量机的癫痫发作预测研究

针对癫痫发作给病人带来的巨大伤害，为临床治疗留下足够空余时间，提出一个可以预测癫痫发作的系统模型。对21名癫痫病人进行研究，提取具有较低算法复杂度的排列熵构成特征向量，将其输入支持向量机（support vector machine，SVM）训练出学习模型，用来识别发作期样本，利用投票机制充分考虑病人差异来判断所处状态，最终实现癫痫的实时预测。结果表明，其中81%的发作可以提前平均50多分钟预测到，且具有较低的误报率。为癫痫发作预测系统的理论研究打下坚实基础。

Epileptic seizure prediction research based on permutation entropy and support vector machine

Aiming 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.