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

Pablo M. Casillas-Espinosa, Armen Sargsyan, Dmitri Melkonian, Terence J. O'Brien

Research output: Contribution to journalArticleResearchpeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)783-791
Number of pages9
JournalEpilepsia
Volume60
Issue number4
DOIs
Publication statusPublished - 1 Apr 2019

Keywords

  • automated seizure detection
  • genetic absence epilepsy rats from Strasbourg
  • posttraumatic epilepsy
  • post–status epilepticus model
  • traumatic brain injury
  • Wistar Albino Glaxo from Rijswijk

Cite this

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title = "A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy",
abstract = "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.",
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A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy. / Casillas-Espinosa, Pablo M.; Sargsyan, Armen; Melkonian, Dmitri; O'Brien, Terence J.

In: Epilepsia, Vol. 60, No. 4, 01.04.2019, p. 783-791.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - A universal automated tool for reliable detection of seizures in rodent models of acquired and genetic epilepsy

AU - Casillas-Espinosa, Pablo M.

AU - Sargsyan, Armen

AU - Melkonian, Dmitri

AU - O'Brien, Terence J.

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N2 - 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.

AB - 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.

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KW - genetic absence epilepsy rats from Strasbourg

KW - posttraumatic epilepsy

KW - post–status epilepticus model

KW - traumatic brain injury

KW - Wistar Albino Glaxo from Rijswijk

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