摘要
癫痫发作具有突发性和反复性,对患者生命安全构成巨大威胁。为了对癫痫发作进行有效地预测,提出了多变量希尔伯特频域模型的癫痫发作预测方法。将希尔伯特边际谱、希尔伯特边际谱的变化方向和希尔伯特加权频率组成一个三维特征向量作为多变量希尔伯特频域模型,输入到支持相量机中,实现癫痫的发作预测,最后采用癫痫发作预测特征方法对预测结果进行评估。实验结果表明：采用多变量希尔伯特频域模型分析方法预测δ波和θ波的癫痫发作,癫痫预测范围在30～45 min,患者有足够的时间采取措施应对；癫痫发作周期在5～10 min,缩短患者等待时间,降低焦虑程度；与多种相关方法进行比较,该方法具有较低的错误预报率和较高的预测敏感度。
Epileptic seizure with sudden and repeatability poses a great threat to patient safety.To effectively predict the epileptic seizure, an epileptic seizure prediction method based on multivariate Hilbert frequency domain model was proposed.Hilbert marginal spectrum,Hilbert weighted frequency and Hilbert marginal spectrum change direction were composed to a three dimensional feature vector as multivariate Hilbert frequency domain model,and then put it into support vector machine （SVM）to prediction epileptic seizure.The epileptic seizure prediction method was used to assess the prediction results.Experimental results showed that when the multivariate Hilbert frequency domain model was used to predict epileptic seizure for δrhythm andθrhythm,the seizure prediction horizon was 30 ~45 minutes,so that patients could have enough time to take measures to deal with seizures.The seizure occurrence period was 5 ~10 minutes, thus,the waiting time was shortened and the anxiety of patient was reduced.Compared with a variety of relevant methods,this method has lower false prediction rate and higher prediction sensitivity.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2015年第10期1383-1387,共5页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61071057)
辽宁省博士启动基金资助项目(201134121)
关键词
脑电信号
希尔伯特黄变换
经验模态分解
希尔伯特边际谱
希尔伯特加权频率
electroencephalogram
Hilbert-Huang transform
empirical mode decomposition
Hilbert marginal spectrum
Hilbert weighted frequency
