A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy.

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 EEGs 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.

A spike is a spike: On the universality of spike features in four epilepsy models.

OBJECTIVE: Frequency properties of the EEG characteristics of different seizure types including absence seizures have been described for various rodent models of epilepsy. However, little attention has been paid to the frequency properties of individual spike-wave complexes (SWCs), the constituting elements characterizing the different generalized seizure types. Knowledge of their properties is not only important for understanding the mechanisms underlying seizure generation but also for the identification of epileptiform activity in various seizure types. Here, we compared the frequency properties of SWCs in different epilepsy models. METHODS: A software package was designed and used for the extraction and frequency analysis of SWCs from long-term EEG of four spontaneously seizing, chronic epilepsy models: a post-status epilepticus model of temporal lobe epilepsy, a lateral fluid percussion injury model of post-traumatic epilepsy, and two genetic models of absence epilepsy-GAERS and rats of the WAG/Rij strain. The SWCs within the generalized seizures were separated into fast (three-phasic spike) and slow (mostly containing the wave) components. Eight animals from each model were used (32 recordings, 104 510 SWCs in total). A limitation of our study is that the recordings were hardware-filtered (high-pass), which could affect the frequency composition of the EEG. RESULTS: We found that the three-phasic spike component was similar in all animal models both in time and frequency domains, their amplitude spectra showed a single expressed peak at 18-20 Hz. The slow component showed a much larger variability across the rat models. SIGNIFICANCE: Despite differences in the morphology of the epileptiform activity in different models, the frequency composition of the spike component of single SWCs is identical and does not depend on the particular epilepsy model. This fact may be used for the development of universal algorithms for seizure detection applicable to different rat models of epilepsy. PLAIN LANGUAGE SUMMARY: There is a large variety between people with epilepsy regarding the clinical manifestations and the electroencephalographic (EEG) phenomena accompanying the epileptic seizures. Here, we show that one of the EEG signs of epilepsy, an epileptic spike, is universal, since it has the same shape and frequency characteristics in different animal models of generalized epilepsies, despite differences in recording sites and location.