Epilepsy Detection with Personal Identification Based on Regularized O-minus Decomposition

Epilepsy is a common neurological disease that seriously affects the patient’s life quality. Electroencephalogram (EEG) is an important modality for epilepsy diagnosis and treatment. Here, we propose a tensor-based method using EEG, which can detect patients with seizures and identify them. In this method, we transform EEG signals into high-order tensors and design a regularized O-minus tensor network to calculate representative signals of channels. The interactive information among all tensor modes can be extracted by the regularized O-minus. Finally, the brain network is calculated based on the extracted representative signals and used as a shared feature set for epilepsy detection and patient identity. Leave one subject out cross-validation is used in seizure detection to verify the generalization of the model for first-time patients. The accuracies of seizure detection and patient identification are 88.14% and 98.59%, respectively. To our knowledge, this is the first time that epilepsy detection and patient identification are implemented within a unified framework, which could provide timely and personalized treatment for patients.