Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
For clinicians diagnosing epilepsy when routine EEGs lack interictal epileptiform discharges, this work provides a reproducible, objective classification method that leverages stimulation-evoked activity.
The paper proposes a machine-learning pipeline using multi-domain features and stacked ensembles to classify epilepsy from EEG recordings during intermittent photic stimulation (IPS) and hyperventilation (HV), achieving up to 97.8% AUC on IED-free resting-state EEG and 94.1% AUC on IED-free IPS on the TUH dataset.
Diagnosing epilepsy is challenging when routine EEGs lack interictal epileptiform discharges (IEDs). Intermittent photic stimulation (IPS) and hyperventilation (HV) can increase diagnostic yield, but their interpretation is subjective. We propose a reproducible pipeline that classifies EEG recordings acquired during stimulation procedures, using machine-learning features spanning temporal, spectral, wavelet, and connectivity domains, and a stacked ensemble to combine complementary feature sets. Performance is evaluated with leave-one-subject-out (LOSO) cross-validation on the TUH Epilepsy Corpus and a clinical Erasmus MC (EMC) cohort, including IED-free analyses on TUH. On TUH, ensembles achieve up to 97.8\% AUC / 93.1\% BAC on IED-free resting-state EEG and 94.1\% AUC / 86.8\% BAC on IED-free IPS. On EMC, IPS provides the strongest discrimination (79.4\% AUC / 73.9\% BAC), while HV performance benefits from stratifying subjects by responsiveness. These results indicate that stimulation-evoked activity, particularly IPS, contains meaningful discriminative information for IED-free epilepsy classification and that multi-domain ensembling improves robustness.