CAJ | 학술논문

针对经验模态分解在对脑电数据进行处理时存在的端点效应问题，提出了一种新的端点效应抑制方法。该方法将支持向量机和数据加窗进行结合对原始信号进行处理。该方法包括三个步骤：采用支持向量机对原始信号两端分别延拓有限个极大值和极小值；用窗函数对延拓后的数据进行加窗处理；分别对原始信号以及支持向量机延拓和加窗处理后的信号进行经验模态分解，并舍弃各阶固有模态函数中延拓的数据点。为了分析所提方法的性能，以正交性作为量化评价指标对比不同方法的性能。以人工信号和实际脑电信号为实验对象进行的模拟实验表明，相比于其他几种方法，提出的方法可有效抑制经验模态分解处理过程中端点效应问题。
침대경험모태분해재대뇌전수거진행처리시존재적단점효응문제，제출료일충신적단점효응억제방법。해방법장지지향량궤화수거가창진행결합대원시신호진행처리。해방법포괄삼개보취：채용지지향량궤대원시신호량단분별연탁유한개겁대치화겁소치；용창함수대연탁후적수거진행가창처리；분별대원시신호이급지지향량궤연탁화가창처리후적신호진행경험모태분해，병사기각계고유모태함수중연탁적수거점。위료분석소제방법적성능，이정교성작위양화평개지표대비불동방법적성능。이인공신호화실제뇌전신호위실험대상진행적모의실험표명，상비우기타궤충방법，제출적방법가유효억제경험모태분해처리과정중단점효응문제。
In order to restrain the problem of end effect in the Empirical Mode Decomposition(EMD)process of the EEG data, a new method is proposed. This method combines Support Vector Machine(SVM)and window function to deal with the original signal. The method consists of three steps:the signal is forward and backward extended respectively for several extreme points by SVM;window function is added to the extended data;the empirical mode decomposition is used to process the original signal, and the extended signal by the proposed method respectively, and then remove the extend-ed points from the intrinsic mode functions. In order to analyze the performance of the proposed method, orthogonality is chosen as the quantitative evaluation index. The artificial and real EEG signals are used as experimental objects of simula-tion experiments. Comparing with the other methods, the experimental resluts show that the proposed method can effec-tively restrain the end effects during the process of empirical mode decomposition.