A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model

Xanthopoulos, Petros; Liu, Chang-Chia; Zhang, Jicong; Miller, Eric R.; Nair, S. P.; Uthman, Basim M.; Kelly, Kenneth G.; Pardalos, Pãnos M.

doi:10.1109/iembs.2009.5334941

Cited by 12 publications

(17 citation statements)

References 15 publications

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“…Whether the lower selectivity of CesB1 is advantageous for producing cereulide homologues with altered activity is of ongoing interest. Some synthetically produced homologues of cereulide show a marked decrease in toxicity towards HEp2-cells while keeping their ionophoric activity [ 36 ], illustrating the potential utility of homologues of cereulide.…”

Section: Resultsmentioning

confidence: 99%

Characterization of Cereulide Synthetase, a Toxin-Producing Macromolecular Machine

2015

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Cereulide synthetase is a two-protein nonribosomal peptide synthetase system that produces a potent emetic toxin in virulent strains of Bacillus cereus. The toxin cereulide is a depsipeptide, as it consists of alternating aminoacyl and hydroxyacyl residues. The hydroxyacyl residues are derived from keto acid substrates, which cereulide synthetase selects and stereospecifically reduces with imbedded ketoreductase domains before incorporating them into the growing depsipeptide chain. We present an in vitro biochemical characterization of cereulide synthetase. We investigate the kinetics and side chain specificity of α-keto acid selection, evaluate the requirement of an MbtH-like protein for adenylation domain activity, assay the effectiveness of vinylsulfonamide inhibitors on ester-adding modules, perform NADPH turnover experiments and evaluate in vitro depsipeptide biosynthesis. This work also provides biochemical insight into depsipeptide-synthesizing nonribosomal peptide synthetases responsible for other bioactive molecules such as valinomycin, antimycin and kutzneride.

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Section: Resultsmentioning

confidence: 99%

Characterization of Cereulide Synthetase, a Toxin-Producing Macromolecular Machine

2015

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“…Seizure detection algorithms have been described in the literature, 23,24 most of which are aimed at human EEG analysis; very few are designed particularly for rodents. [25][26][27][28][29][30][31][32][33][34] However, no method has been described to detect all of the seizures in different animal models.…”

Section: Discussionmentioning

confidence: 99%

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

Casillas‐Espinosa

Sargsyan²,

Melkonian³

et al. 2019

Epilepsia

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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 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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“…Knowing the number of seizures and duration of each seizure is a key factor to evaluate the efficacy of a drug or other interventional therapy in order to treat seizure disorders. Generally, visual detection on absence seizures is less practical due to their brief duration, high frequency of occurrence and subtle clinical manifestations (Xanthopoulos et al 2009). So development of an algorithm that is able to automatically detect these types of seizures with an acceptable accuracy is an effective support to epileptic patients.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, various methods have been applied to detect absence seizures in both human and animal subjects. The time-frequency domain approaches are the most appropriate method to analyze non-stationary signals such as EEG and electrocardiography (ECG) (Van Hese et al 2003;Xanthopoulos et al 2009;Ovchinnikov et al 2010).…”

Section: Introductionmentioning

confidence: 99%

An autonomous real-time single-channel detection of absence seizures in WAG/Rij rats

Aghazadeh

Shahabi²,

Frounchi³

et al. 2015

gpb

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This paper presents a real-time, completely automated and patient independent algorithm for detection of absence seizures in WAG/Rij rats as a valid animal model of human absence epilepsy. Single-channel EEG recordings containing totally 488 seizures from 8 WAG/Rij rats were analyzed using the real-time SWD detection algorithm. The proposed algorithms based on the variation of wavelet power to the background power in two specific frequency bands whose spectral power are highly correlated with SWDs. The wavelet powers of two specific frequency bands are calculated with a pattern-adapted mother wavelet and compared with an adaptive ratio of background power of each frequency band. The results indicate used algorithm is able to detect the whole 488 seizures within less than 1 s with sensitivity of 100%. The average precision for 1200, 1400 and 1600 point of window size was 95.2%, 98.3% and 99.17%, respectively. The present algorithm, with its high sensitivity and specificity, could be used for further studies of absence seizures in humans and rats and could be implemented as real-time system for closed loop deep brain stimulation systems.

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A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model

Cited by 12 publications

References 15 publications

Characterization of Cereulide Synthetase, a Toxin-Producing Macromolecular Machine

Characterization of Cereulide Synthetase, a Toxin-Producing Macromolecular Machine

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

An autonomous real-time single-channel detection of absence seizures in WAG/Rij rats

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