东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2015, Vol. 36 ›› Issue (10): 1383-1387.DOI: 10.3969/j.issn.1005-3026.2015.10.004

• 信息与控制 • 上一篇    下一篇

基于多变量希尔伯特频域模型的癫痫发作预测

韩凌1， 王宏2， 李春胜3   

  1. (1. 东北大学 中荷生物医学工程与信息学院， 辽宁 沈阳110819； 2. 东北大学 机械工程与自动化学院， 辽宁 沈阳110819； 3. 沈阳工业大学 电气工程学院， 辽宁 沈阳110870)
  • 收稿日期:2014-09-23 修回日期:2014-09-23 出版日期:2015-10-15 发布日期:2015-09-29
  • 通讯作者: 韩凌
  • 作者简介:韩凌(1980-)，女，辽宁沈阳人，东北大学博士研究生; 王宏(1960-)，女，辽宁沈阳人，东北大学教授，博士生导师.
  • 基金资助:

    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).

Epileptic Seizure Prediction Based on Multivariate Hilbert Frequency Domain Model

HAN Ling1， WANG Hong2， LI Chun-sheng3   

  1. 1. School of Sino-Dutch Biomedical & Information Engineering， Northeastern University， Shenyang 110819， China; 2. School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China; 3. School of Electrical Engineering， Shenyang University of Technology， Shenyang 110870， China.
  • Received:2014-09-23 Revised:2014-09-23 Online:2015-10-15 Published:2015-09-29
  • Contact: WANG Hong
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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30~45min，患者有足够的时间采取措施应对；癫痫发作周期在5~10min，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.

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

Abstract:

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 δ rhythm and θ rhythm， the seizure prediction horizon was 30~45minutes， so that patients could have enough time to take measures to deal with seizures. The seizure occurrence period was 5~10minutes， 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.

Key words: electroencephalogram, Hilbert-Huang transform, empirical mode decomposition, Hilbert marginal spectrum, Hilbert weighted frequency

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