Localizing seizure onset zone by convolutional transfer entropy from iEEG.

Automatic localization of the seizure onset zone (SOZ) is able to output an objective result and help clinical doctors greatly in epilepsy therapy. Transfer entropy is one of the most frequently used measures based on information theory to localize the SOZ. However, if only using transfer entropy to localize the SOZ, different results can be obtained during different periods, thus humans still need to identify which one is most reasonable. This paper proposes a new method to output only a few (e.g. 1 or 2) results along a long time slot. Based on the results of traditional transfer entropy, we use a 3D convolution method to enhance the connection between the spatial channels and also between different temporal positions. After that, a connected component method is used to extract the stable blocks that indicate the SOZ. To evaluate the effectiveness of our method, preliminary experiments on a short iEEG signals are conducted. The experimental results show that our method can achieve a sensitivity of 100% and a false positive rate of 1.79% for SOZ localization.