Spike separation from EEG/MEG data using morphological filter and wavelet transform.
Spike separation from EEG/MEG data using morphological filter and wavelet transform.
复制标题
使用形态滤波器和小波变换从 EEG/MEG 数据中分离尖峰。
DOI：
10.1109/iembs.2006.259695
复制
发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Sun,Mingui
中科院分区：
文献类型：
--
作者：
Jia,Wenyan;Sclabassi,RobertJ;Pon,Lin-Sen;Scheuer,MarkL;Sun,Mingui
在癫痫脑电图（EEG）和脑磁图（MEG）数据的分析中，棘波分离在诊断上是重要的，因为癫痫灶的定位通常依赖于从原始数据中准确提取棘波活动。本文提出了一种基于圆形结构元的小波变换与形态滤波相结合的棘波自动提取方法。实验结果表明，该方法在尖峰信号分离中是非常有效的。与小波、带通滤波、经验模态分解（EMD）和独立分量分析（伊卡）方法的比较表明，该方法在估计棘波幅度和位置方面都更有效
In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations