Abstract found on Wiley Online Library
Objective: With the advent of ultra-long-term recordings for monitoring of epilepsies, the interpretation of results of isolated EEG recordings covering only selected brain regions attracts considerable interest. In this context, the question arises whether detected ictal [related to a seizure] EEG patterns correspond to clinically manifest seizures or rather to purely electrographic events, i.e. subclinical events.
Methods: The analysis of EEG patterns from 268 clinical seizures and 252 subclinical electrographic events from 50 patients undergoing video-EEG monitoring. Features extracted included predominant frequency band, duration, association with rhythmic muscle artifacts, spatial extent and propagation patterns. Classification using logistic regression was performed based on data from the whole dataset of 10-20-EEG recordings and from a subset of two temporal electrode contacts.
Results: Correct separation of clinically manifest and purely electrographic events based on 10-20-EEG recordings was possible in up to 83.8% of events, depending on the combination features included. Correct classification based on two-channel recordings was only slightly inferior, achieving 78.6% accuracy 74.4 and 74.8%, respectively, of events could be correctly classified when using duration alone with either electrode set, but classification accuracies were lower for some subgroups of seizures, particularly focal aware seizures and epileptic arousals.
Significance: A correct classification of subclinical vs. clinical EEG events was possible in 74-83% of events based on full EEG recordings, and in 74-78% when considering only a subset of 2 electrodes, matching the channel number available from new implantable diagnostic devices. This is a promising outcome suggesting that ultra-long-term low-channel EEG recordings may provide sufficient information for objective seizure diaries. Intra-individual optimization using high numbers of ictal events may further improve separation, provided that supervised learning with external validation is feasible.