Explainable Seizure Detection from Intracranial EEG Using a Spatio-Temporal Model

Seizure detection based on intracranial electroencephalography (iEEG) signals is a relevant task in the analysis of epilepsy. In this context, it is not only important to achieve high predictive performance but also to ensure explainability, which allows for the analysis of the model’s behavior. The properties of the problem allow it to be formulated as a spatio-temporal problem due to the multichannel nature of iEEG and the temporal evolution of epileptic activity. Therefore, the data must be modeled jointly due to spatial and temporal dependencies. In this work, we propose the Exact Self Explainable Graph Convolutional Recurrent Network (ESEGCRN) for the detection of ictal and interictal periods in a patient-specific setting. The model represents the iEEG channels as nodes in a graph and the temporal evolution of the signal as a sequence over that structure. To validate the proposal, ESEGCRN is compared with various models that address the same problem. The results show that our model achieves the best overall predictive performance among the compared models. Furthermore, our model incorporates an internal explainability mechanism that generates a mask allowing for the analysis of node relevance. Analysis of the mask shows that, as the use of connections is restricted, incoming edges tend to progressively concentrate on seizure onset zone (SOZ) nodes. This reinforces confidence in the model and suggests that the relevance inferred by ESEGCRN is related to clinically significant nodes.