Wavelet-crosscorrelation analysis: Non-stationary analysis of neurophysiological signals
Wavelet-crosscorrelation analysis: Non-stationary analysis of neurophysiological signals
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DOI：
10.1007/s10548-005-6032-2
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发表时间：
2005-06-01
期刊：
影响因子：
2.7
通讯作者：
Inouye, T
中科院分区：
文献类型：
--
作者：
Mizuno-Matsumoto, Y;Ukai, S;Inouye, T
目的：小波互相关分析是小波分析的一个新的应用，用于显示癫痫放电的传播和定位相应的病变。我们之前已经证明，这种分析可以帮助预测大脑状况的统计（Mizuno-Matsumoto et al. 2002）。我们的目的是评估小波互相关分析是否揭示了人类患者癫痫样活动的启动和传播。研究方法：本文用小波互相关方法分析了3例单纯部分性发作（SPS）患者的全脑磁图（MEG）。确定了64个脑磁通道在不同时间段的小波互相关系数（WCC）、每对可能信号的相干结构以及两个相关信号的时滞（M）。结果：我们清楚地显示了刺激区的定位和癫痫样放电的传播。结论：小波互相关分析有助于揭示和可视化脑状态的动态变化。这种分析方法可以弥补其他现有的分析方法的MEG，脑电图（EEG）或皮质电图（ECoG）。重要性：我们提出的方法表明，揭示和可视化大脑状况的动态变化可以帮助临床医生甚至患者自己更好地了解这些状况。
Objective: Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptifonn discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients. Methods: The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels for various periods, and the time lag (M) in two related signals, were ascertained. Results: We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges. Conclusions: Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG). Significance: Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.