Characterization of early partial seizure onset: Frequency, complexity and entropy

ObjectiveA clear classification of partial seizures onset features is not yet established. Complexity and entropy have been very widely used to describe dynamical systems, but a systematic evaluation of these measures to characterize partial seizures has never been performed.MethodsEighteen different measures including power in frequency bands up to 300 Hz, Gabor atom density (GAD), Higuchi fractal dimension (HFD), Lempel–Ziv complexity, Shannon entropy, sample entropy, and permutation entropy, were selected to test sensitivity to partial seizure onset. Intracranial recordings from 45 patients with mesial temporal, neocortical temporal and neocortical extratemporal seizure foci were included (331 partial seizures).ResultsGAD, Lempel–Ziv complexity, HFD, high frequency activity, and sample entropy were the most reliable measures to assess early seizure onset.ConclusionsIncreases in complexity and occurrence of high-frequency components appear to be commonly associated with early stages of partial seizure evolution from all regions. The type of measure (frequency-based, complexity or entropy) does not predict the efficiency of the method to detect seizure onset.SignificanceDifferences between measures such as GAD and HFD highlight the multimodal nature of partial seizure onsets. Improved methods for early seizure detection may be achieved from a better understanding of these underlying dynamics.

► This study compares the relative ability of spectral-based measures, entropy measures and complexity measures to detect early seizure onset.
► GAD complexity, fractal dimension, sample entropy and high-frequency power are the most reliable measures to assess ictal activity.
► Early seizure detection needs a multimodal approach to better understand partial seizure onsets and improve efficiency.