ABSTRACT
The objective is to deal with brain effective connectivity among epilepsy electroencephalogram (EEG) signals recorded by use of depth electrodes in the cerebral cortex of patients suffering from refractory epilepsy during their epileptic seizures. The Wiener-Granger Causality Index (WGCI) is a well-known effective measure that can be useful to detect causal relations of interdependence in these kinds of EEG signals. It is based on the linear autoregressive model, and the issue of the estimation of the model parameters plays an important role in the calculation accuracy and robustness of WGCI to do research on brain effective connectivity. Focusing on this issue, a modified Akaike's information criterion algorithm is introduced in the computation of the WGCI to estimate the orders involved in the underlying models and in order to advance the performance of WGCI to detect brain effective connectivity. Experimental results support the interesting performance of the proposed algorithm to characterize the information flow both in a linear stochastic system and a physiology-based model.
ABSTRACT
Objective To provide new diagnose informations for clinician by using image fusion. Methods 30 cases with brain lesion (men 18,women were studied 12). Twenty cases were examined by CT or MR in 1_2 weeks, the others were checked by MR, after being diagnosed with CT. These image data were input into computer the center and main axis of image were located by Legendre moment then the CT and MR images were registered by translation, scaling and rotation. Results Of 30 image fusions, supplementary informations were provided to each other in 28 cases,prediction of disease progress in 19 cases. There were 4 cases confirmed at surgery. But in 2 cases no obvious advantage was obtained by image fusion.Conclusion CT and MR image fusion provides useful information for definitive diagnosis, planning of surgery and radiotherapy. To fuse the images of CT and MR, the Legendre moment approach is direct, simple and fast.