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A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy
Author(s) -
CasillasEspinosa Pablo M.,
Sargsyan Armen,
Melkonian Dmitri,
O'Brien Terence J.
Publication year - 2019
Publication title -
epilepsia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.687
H-Index - 191
eISSN - 1528-1167
pISSN - 0013-9580
DOI - 10.1111/epi.14691
Subject(s) - epilepsy , electroencephalography , status epilepticus , neuroscience , medicine , artifact (error) , genetic model , psychology , biology , biochemistry , gene
Summary Objective Prolonged electroencephalographic ( EEG ) monitoring in chronic epilepsy rodent models has become an important tool in preclinical drug development of new therapies, in particular those for antiepileptogenesis, disease modification, and treating drug‐resistant epilepsy. We have developed an easy‐to‐use, reliable, computational tool for automated detection of electrographic seizures from prolonged EEG recordings in rodent models of epilepsy. Methods We applied a novel method based on advanced time‐frequency analysis that detects EEG episodes with excessive activity in certain frequency bands. The method uses an innovative technique of short‐term spectral analysis, the Similar Basis Function algorithm. The method was applied for offline seizure detection from long‐term EEG recordings from four spontaneously seizing, chronic epilepsy rat models: the fluid percussion injury (n = 5 rats, n = 49 seizures) and post–status epilepticus models (n = 119 rats, n = 993 seizures) of acquired epilepsy, and two genetic models of absence epilepsy, Genetic Absence Epilepsy Rats from Strasbourg and Wistar Albino Glaxo from Rijswijk (n = 41 and 14 rats, n = 8733 and 825 seizures, respectively). Results Our comparative analysis revealed that the EEG amplitude spectra of these four rat models are remarkably similar during epileptiform activity and have a single expressed peak within the 17‐ to 25‐Hz frequency range. Focusing on this band, our computer program detected all seizures in the 179 rats. A quick semiautomated user inspection of the EEG s for the period of each identified event allowed quick rejection of artifact events. The overall processing time for 12‐day‐long recordings varied from a few minutes (5‐10) to 30 minutes, depending on the number of artifact events, which was strongly correlated with the signal quality of the raw EEG data. Significance Our automated seizure detection tool provides high sensitivity, with acceptable specificity, for long‐ and short‐term EEG recordings from both acquired and genetic chronic epilepsy rat models. This tool has the potential to improve the efficiency and rigor of preclinical research and therapy development using these models.