Epilepsiformer

Epilepsiformer is a term used in some research contexts to describe transformer-based artificial intelligence models applied to electroencephalography (EEG) data to detect epileptiform activity and assist in seizure management. As a term, it refers to a class of models rather than a single implementation.

These models process multi-channel EEG data as sequences or spectro-temporal features and use self-attention to capture long-range temporal dependencies and inter-channel relationships. Some designs combine convolutional layers with transformer blocks to extract local patterns before applying attention.

Training typically relies on annotated datasets from epilepsy patients, including interictal epileptiform discharges and seizures. Data augmentation, cross-subject fine-tuning, and transfer learning are common strategies to address limited labeled data and device variability.

Applications include automatic detection of epileptiform events, seizure forecasting, and aiding in localization of seizure onset zones in research settings. Reported benefits include higher sensitivity and lower false-positive rates in some studies, though results vary with data quality and patient heterogeneity.

Limitations and challenges include reliance on large, well-annotated datasets, cross-patient generalization, interpretability of attention mechanisms, and integration into clinical workflows. Epilepsiformer remains an active area of investigation rather than a standardized clinical tool. See also deep learning in epilepsy, EEG analysis, seizure forecasting, and transformer models in time series.