Visualization of the current-density distribution for MCG with WPW syndrome patients using independent component analysis
Visualization of the current-density distribution for MCG with WPW syndrome patients using independent component analysis
复制标题
使用独立成分分析对 WPW 综合征患者的 MCG 电流密度分布进行可视化
DOI：
10.1109/tmag.2004.828990
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发表时间：
2004
影响因子：
2.1
通讯作者：
M. Yoshizawa
中科院分区：
文献类型：
--
作者：
K. Kobayashi;Y. Uchikawa;K. Nakai;M. Yoshizawa
本研究的目的是评估预激综合征（WPW）的旁路。我们用SQUID磁强计测量了正常人和预激综合征患者的心磁图。由于旁道电流密度分布（VCDD）的范围很广，因此通常难以通过可视化来估计旁道。独立分量分析（伊卡）是一种从重叠信号中分离独立信号的有效方法。用伊卡的VCDD对旁道信号源进行估计。用伊卡提取旁路效应，准确估计旁路活性。伊卡能够提取旁路的作用。证实伊卡是一种有效的估计WPW综合征患者MCG VCDD的方法。
The purpose of this study is to evaluate the accessory pathway in the Wolff-Parkinson-White (WPW) syndrome. We measured magnetocardiograms (MCGs) for normal subjects and patients with the WPW syndrome using SQUID magnetometer. It is generally difficult to estimate the accessory pathway by visualization of the current-density distribution (VCDD) because the VCDD has a wide range. Independent component analysis (ICA) is a useful method for separating independent signals from overlapping signals. The source estimation of the accessory pathway was done by the VCDD using ICA. The effect of the accessory pathway was extracted by ICA, and the activity of the accessory pathway was accurately estimated. ICA was able to extract the effect of the accessory pathway. It is confirmed that ICA is a useful method to estimate the VCDD of MCG with WPW syndrome patients.