Combined Anomaly Features and Interictal Epileptiform Discharges for Effective Seizure Onset Zone Localization

Epilepsy, a prevalent neurological disease, often requires accurate identification of the seizure onset zone (SOZ) in the brain for successful surgical removal of this region. SOZ identification is a lengthy process that traditionally relies on the knowledge and experience of the neurosurgeon. Advancements in artificial intelligence have opened the door to automatic SOZ localization. This study proposes the application of autoencoder-based anomaly detection in SOZ electrode classification for the first time. We trained the autoencoder in a leave-one-patient-out manner with electrocorticography (ECoG) signals from intact channels. The anomaly feature was determined by the maximum error between original and reconstructed signals for each channel of the test patient. We investigated the usefulness of anomaly features along with the interictal epileptiform discharge (IED) biomarker feature. A linear support vector machine classifier achieved 70.49% accuracy with the anomaly feature, 64.71% accuracy with IED feature, and 74.44% accuracy with combined anomaly and IED features. The study demonstrates the effectiveness of anomaly detection in the direct localization of SOZs and suggests that multiple biomarker features can enhance automatic SOZ localization performance.Clinical Relevance- This study demonstrates the clinical relevance of using anomaly features from ECoG data for efficient SOZ localization. It highlights the effectiveness of combining anomaly features with IED biomarkers to enhance the automatic SOZ classification performance and improve surgical planning in epilepsy treatment.