Classification of IED-free EEG Responses for Assisted Epilepsy Diagnosis
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 ...
| Authors | Giacomo Zanardini, Ryan Moesman, Paul van der Kleij et al. |
|---|---|
| Primary category | eess.SP |
| Published | 19 May 2026 |
| Last updated at source | 19 May 2026 |
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