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Objective  The electrical characteristics of the EEG signals can be used for seizuredetection. Statistical independence between different brain regions is measuredby functional brain connectivity (FBC). Specific directional effects can'tconsider by FBC and thus effective brain connectivity (EBC) is used to measurecausal intervention between one neuronal region and the rest of the neuronalregions. Our main purpose is to provide a reliable automatic seizure detectionapproach.  Methods  In this study, three new methods are provided. Deep modular neural network(DMNN) is developed based on a combination of various EBC classificationresults in the different frequencies. Another method is named "modulareffective neural networks (MENN)". This method combines the classificationresults of the three different EBC in the specific frequency. "Modularfrequency neural networks (MFNN)" is another method that combines theclassification results of the specific EBC in the seven different frequencies.  Results  The mean accuracy of the MFNN are 97.14%, 98.53%, and 97.91% using directedtransfer function, directed coherence, and generalized partial directedcoherence, respectively. Using the MENN, the highest mean accuracy is 98.34%.Finally, DMNN has the highest mean accuracy which is equal to 99.43. To ourbest knowledge, the proposed method is a new method that provides the highaccuracy in comparison to other studies which used MIT-CHB database.  Conclusion and significance  The knowledge of structure-function relationships between different areas ofthe brain is necessary for characterizing the underlying dynamics. Hence,features based on EBC can provide a reliable automatic seizure detectionapproach.

A framework for seizure detection using effective connectivity, graph theory and deep modular neural networks