AbstractObjectiveMark-up of generalized interictal epileptiform discharges (IEDs) on EEG is an important step in the diagnosis and characterization of epilepsy. However, manual EEG mark-up is a time-consuming, subjective, and highly specialized task where the human reviewer needs to visually inspect a large amount of data to facilitate accurate clinical decisions. The objective of this study was to develop a framework for automated detection of generalized paroxysmal fast activity (GPFA), which is a characteristic type of generalized IED seen in scalp EEG recordings of patients with Lennox-Gastaut syndrome (LGS), a severe form of drug-resistant generalized epilepsy.MethodsWe studied 13 children with LGS who had GPFA events in their interictal EEG recordings. Time-frequency information derived from manually marked IEDs across multiple EEG channels was used to automatically detect similar events in each patient’s interictal EEG. We validated true positives and false positives of the proposed spike detection approach using both standalone scalp EEG and simultaneous EEG-functional MRI (EEG-fMRI) recordings.ResultsGPFA events displayed a consistent low-high frequency arrangement in the time-frequency domain. This ‘bi-modal’ spectral feature was most prominent over frontal EEG channels. Our automatic detection approach using this feature identified likely epileptic events with similar time-frequency properties to the manually marked GPFAs. Brain maps of EEG-fMRI signal change during these automatically detected IEDs were comparable to the EEG-fMRI brain maps derived from manual IED mark-up.ConclusionGPFA events have a characteristic bi-modal time-frequency feature that can be automatically detected from scalp EEG recordings in patients with LGS. Validity of this time-frequency feature is demonstrated by EEG-fMRI analysis of automatically detected events, which recapitulates the brain maps we have previously shown to underlie generalized IEDs in LGS.SignificanceThis study provides a novel methodology that paves the way for quick, automated, and objective inspection of generalized IEDs in LGS. The proposed framework may be extendable to a wider range of epilepsy syndromes in which monitoring the burden of epileptic activity can aid clinical decision-making. For example, automated quantification of generalized discharges may permit faster assessment of treatment response and estimation of future seizure risk.
Automatic detection of generalized paroxysmal fast activity in interictal EEG using time-frequency analysis
