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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. 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 (TL) in two related signals, were ascertained. We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges. 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). 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. [ABSTRACT FROM AUTHOR]

Objectives: Introduction of a new grid-based method for analyzing speech functions which takes into account the related information of patients' data and the oral air flow with pronouncing analyzed by computational fluid dynamics.Methods: An on-line speech analyzer was developed for clinical use utilizing GridPort2.3.1 based on globus2.4.2, comprising several computational tools such as unified data storage, semantic data analysis, computational fluid dynamics analysis and three-dimensional visualization of calculated results from different hardware sources with various types of operation systems.Results: The power transportation layer between dental clinics and computational and storage resources could be provided by using a WWW-based portal. The backend data management system could be constructed using a storage resource broker (SRB) and extensible mark up language (XML).Conclusions: The new method allows the construction of a data warehouse through this grid-based speech function analysis in order to extract the principal factors related to speech disorders. [ABSTRACT FROM AUTHOR]