Objective: An important issue in epileptology is the question whether informat ion extracted from the EEG of epilepsy patients can be used for the prediction o f seizures. Several studies have claimed evidence for the...Objective: An important issue in epileptology is the question whether informat ion extracted from the EEG of epilepsy patients can be used for the prediction o f seizures. Several studies have claimed evidence for the existence of a pre-se izure state that can be detected using different characterizing measures. In thi s paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both li near and non-linear approaches. Methods: We compared 30 measures in terms of th eir ability to distinguish between the interictal period and the pre-seizure pe riod. After completely analyzing continuous inctracranial multi-channel recordi ngs from five patients lasting over days, we used ROC curves to distinguish betw een the amplitude distributions of interictal and preictal time profiles calcula ted for the respective measures. We compared different evaluation schemes includ ing channelwise and seizurewise analysis plus constant and adaptive reference le vels. Particular emphasis was placed on statistical validity and significance.Re sults: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhib ited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 mi n before seizures. Linear measures were found to perform similar or better than nonlinear measures. Conclusions: Results provide statistically significant evide nce for the existence of a preictal state. Based on our findings, the most promi sing approach for prospectiveseizure anticipation could be a combination of biva riate and univariate measures. Significance: Many measures reported capable of s eizure prediction in earlier studies are found to be insignificant in performanc e,which underlines the need for sta展开更多
1 TOPSIS本身 TOPSIS法(Technique for Order Preference bySimilarity to Ideal Solution)的意思是“与理想方案相似性的顺序选优技术”,它是系统工作中有限方案多目标决策分析中用到的一种决策技术,曾在工业经济效益的综合评价中得到...1 TOPSIS本身 TOPSIS法(Technique for Order Preference bySimilarity to Ideal Solution)的意思是“与理想方案相似性的顺序选优技术”,它是系统工作中有限方案多目标决策分析中用到的一种决策技术,曾在工业经济效益的综合评价中得到应用。其原理系基于归一化后的原始数据矩阵,找出有限方案中的最优方案和最劣方案构成一个空间,而待评价的某方案则可视为该空间上的一个点,据此可获得该点与最优方案和最劣方案间的距离(常用Euclidean距离,又称欧氏距离),从而得出该方案与最优方案的相对接近程度,籍此可对该方案作出优劣的评价。展开更多
文摘Objective: An important issue in epileptology is the question whether informat ion extracted from the EEG of epilepsy patients can be used for the prediction o f seizures. Several studies have claimed evidence for the existence of a pre-se izure state that can be detected using different characterizing measures. In thi s paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both li near and non-linear approaches. Methods: We compared 30 measures in terms of th eir ability to distinguish between the interictal period and the pre-seizure pe riod. After completely analyzing continuous inctracranial multi-channel recordi ngs from five patients lasting over days, we used ROC curves to distinguish betw een the amplitude distributions of interictal and preictal time profiles calcula ted for the respective measures. We compared different evaluation schemes includ ing channelwise and seizurewise analysis plus constant and adaptive reference le vels. Particular emphasis was placed on statistical validity and significance.Re sults: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhib ited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 mi n before seizures. Linear measures were found to perform similar or better than nonlinear measures. Conclusions: Results provide statistically significant evide nce for the existence of a preictal state. Based on our findings, the most promi sing approach for prospectiveseizure anticipation could be a combination of biva riate and univariate measures. Significance: Many measures reported capable of s eizure prediction in earlier studies are found to be insignificant in performanc e,which underlines the need for sta
文摘1 TOPSIS本身 TOPSIS法(Technique for Order Preference bySimilarity to Ideal Solution)的意思是“与理想方案相似性的顺序选优技术”,它是系统工作中有限方案多目标决策分析中用到的一种决策技术,曾在工业经济效益的综合评价中得到应用。其原理系基于归一化后的原始数据矩阵,找出有限方案中的最优方案和最劣方案构成一个空间,而待评价的某方案则可视为该空间上的一个点,据此可获得该点与最优方案和最劣方案间的距离(常用Euclidean距离,又称欧氏距离),从而得出该方案与最优方案的相对接近程度,籍此可对该方案作出优劣的评价。
