Scalp Electroencephalography Markers of Hidden Interictal Epileptiform Discharges in the Mesial Temporal Lobe

Interictal epileptiform discharges (IEDs) are crucial for epilepsy diagnosis but are often undetectable on scalp EEG (scEEG). This study aims to develop a Support Vector Machine classifier to detect mesial temporal lobe (MTL) IEDs invisible on scEEG using simultaneous intracranial EEG (iEEG) and scEEG recordings and to identify contributing scEEG features. Data from 17 patients with epilepsy were analyzed. IED epochs were extracted where IEDs were present on iEEG but absent on scEEG. Control epochs were selected from periods without IEDs on both Electroencephalographies (EEGs). Feature selection was performed, and a classifier was developed and validated with external data from 35 MTL epilepsy (MTLE) and 33 nonepileptic patients. The classifier used 58 selected features and achieved an accuracy of 0.70 and an area under the receiver operating characteristic curve of 0.78 on holdout validation. External validation revealed significant differences in IED-classified epoch frequencies before and after drug withdrawal in patients with MTLE, and between MTLE and nonepileptic groups. Feature analysis identified high-frequency power suppression, increased ipsilateral connectivity, and enhanced cross-frequency coupling as markers of IEDs that were undetectable in scEEG. This study shows that machine learning can detect MTL IEDs invisible to scEEG, revealing related scEEG changes and aiding EEG analysis.