Mizuno-Matsumoto Y, Ukai S, Ishii R, Date S, Kaishima T, Shinosaki K, Shimojo S, Takeda M, Tamura S, Inouye T. Wavelet-Crosscorrelation Analysis: Non-Stationary Analysis of Neurophysiological Signals.
Brain Topogr 2005;
17:237-52. [PMID:
16110773 DOI:
10.1007/s10548-005-6032-2]
[Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
OBJECTIVE
Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform 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 forvarious periods, and the time lag (TL) 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.
Collapse