Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury

White, Andrew M.; Williams, Philip A.; Ferraro, Damien; Clark, Shawn P.; Kadam, Shilpa D.; Dudek, F. Edward; Staley, Kevin J.

doi:10.1016/j.jneumeth.2005.09.014

Cited by 126 publications

(139 citation statements)

References 40 publications

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“…DClamp (sourceforge.net) was used to detect seizures using supervised algorithms 33. Detection parameters were set to increase sensitivity to the point that seizures were rarely missed in trial detections, but false‐positive detections were common.…”

Section: Methodsmentioning

confidence: 99%

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Staged anticonvulsant screening for chronic epilepsy

Berdichevsky

Saponjian

Park

et al. 2016

Ann Clin Transl Neurol

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ObjectiveCurrent anticonvulsant screening programs are based on seizures evoked in normal animals. One‐third of epileptic patients do not respond to the anticonvulsants discovered with these models. We evaluated a tiered program based on chronic epilepsy and spontaneous seizures, with compounds advancing from high‐throughput in vitro models to low‐throughput in vivo models.MethodsEpileptogenesis in organotypic hippocampal slice cultures was quantified by lactate production and lactate dehydrogenase release into culture media as rapid assays for seizure‐like activity and cell death, respectively. Compounds that reduced these biochemical measures were retested with in vitro electrophysiological confirmation (i.e., second stage). The third stage involved crossover testing in the kainate model of chronic epilepsy, with blinded analysis of spontaneous seizures after continuous electrographic recordings.ResultsWe screened 407 compound‐concentration combinations. The cyclooxygenase inhibitor, celecoxib, had no effect on seizures evoked in normal brain tissue but demonstrated robust antiseizure activity in all tested models of chronic epilepsy.InterpretationThe use of organotypic hippocampal cultures, where epileptogenesis occurs on a compressed time scale, and where seizure‐like activity and seizure‐induced cell death can be easily quantified with biomarker assays, allowed us to circumvent the throughput limitations of in vivo chronic epilepsy models. Ability to rapidly screen compounds in a chronic model of epilepsy allowed us to find an anticonvulsant that would be missed by screening in acute models.

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Section: Methodsmentioning

confidence: 99%

“…Repeat biochemical as well as electrophysiological confirmation in vitro comprised the second stage of screening. The final stage was comprised of double blind, crossover controlled, in vivo testing in the kainate model of severe chronic epilepsy9, 32 with seizure quantification by continuous electrographic monitoring 33…”

Section: Introductionmentioning

confidence: 99%

Staged anticonvulsant screening for chronic epilepsy

Berdichevsky

Saponjian

Park

et al. 2016

Ann Clin Transl Neurol

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“…Method 1 by Osorio and Frei [16] is in fact a detection method proposed for human seizures, but it relies on features that are shared with epileptic seizures from rats and has been previously applied on rat data in [18] and [21]. Method 2 by White [17] has been specifically designed for detecting epileptic seizures in rats. To our knowledge, this is the most widely used technique because it can easily be implemented and performs reasonably well.…”

Section: Other Detection Methodsmentioning

confidence: 99%

“…Moreover the performance is evaluated on a much lager dataset: 452 hours of EEG from 23 GAERS and 982 hours of EEG from 15 kainate-induced epilepsy rats as opposed to respectively 15 hours from 13 rats and 4.5 hours from 4 rats. To show the detection performance a comparison is made with seven other seizure detection methods: a human seizure detection method presented in [16], the five seizure detection methods presented for rats in [17], [18], [19], [20] and [21] and a seizure detection method based on the method presented in this study. The latter however uses a linear classifier as opposed to RC.…”

Section: Reservoir Computingmentioning

confidence: 99%

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Buteneers

Verstraeten

Mierlo

et al. 2011

Artificial Intelligence in Medicine

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Introduction: In this paper we propose a technique based on reservoir computing (RC) to mark epileptic seizures on the intra-cranial electroencephalogram (EEG) of rats. RC is a recurrent neural networks training technique which has been shown to possess good generalization properties with limited training. Materials: The system is evaluated on data containing two different seizure types: absence seizures from genetic absence epilepsy rats from Strasbourg (GAERS) and tonic-clonic seizures from kainate-induced temporal-lobe epilepsy rats. The dataset consists of 452 hours from 23 GAERS and 982 hours from 15 kainate-induced temporal-lobe epilepsy rats. Methods: During the preprocessing stage, several features are extracted from the EEG. A feature selection algorithm selects the best features, which are then presented as input to the RC-based classification algorithm. To classify the output of this algorithm a two-threshold technique is used. This technique is compared with other state-of-the-art techniques. Results: A balanced error rate (BER) of 3.7% and 3.5% was achieved on the data from GAERS and kainate rats, respectively. This resulted in a sensitivity of 96% and 94% and a specificity of 96% and 99% respectively. The state-of-theart technique for GAERS achieved a BER of 4%, whereas the best technique to detect tonic-clonic seizures achieved a BER of 16%. Conclusion: Our method outperforms up-to-date techniques and only a few parameters need to be optimized on a limited training set. It is therefore suited as an automatic aid for epilepsy researchers and is able to eliminate the tedious manual review and annotation of EEG.

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“…Electrographic seizures often occur with minimal behaviors (certainly without convulsions), which limits the usefulness of assays based on behavioral seizures. The strategy of combining EEG recording and simultaneous video monitoring allows one the possibility of capturing every seizure; and furthermore, these analytical approaches may allow quantitative assessment of the interictal spikes that occur in the epileptic brain between "ictal" (or seizure) events 9 . Furthermore, the ability to obtain continuous high-quality lowartifact EEG recordings, for which the wireless technology is generally superior, will allow for development of use of computer-based algorithms for studying specific EEG waveforms (e.g., theta, gamma), as well as automatic detection of seizures, significantly reducing the workload of the experimenter.…”

Section: Introductionmentioning

confidence: 99%

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

Zayachkivsky

Lehmkuhle

Dudek

2015

JoVE

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Many progressive neurologic diseases in humans, such as epilepsy, require pre-clinical animal models that slowly develop the disease in order to test interventions at various stages of the disease process. These animal models are particularly difficult to implement in immature rodents, a classic model organism for laboratory study of these disorders. Recording continuous EEG in young animal models of seizures and other neurological disorders presents a technical challenge due to the small physical size of young rodents and their dependence on the dam prior to weaning. Therefore, there is not only a clear need for improving pre-clinical research that will better identify those therapies suitable for translation to the clinic but also a need for new devices capable of recording continuous EEG in immature rodents. Here, we describe the technology behind and demonstrate the use of a novel miniature telemetry system, specifically engineered for use in immature rats or mice, which is also effective for use in adult animals.

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Efficient unsupervised algorithms for the detection of seizures in continuous EEG recordings from rats after brain injury

Cited by 126 publications

References 40 publications

Staged anticonvulsant screening for chronic epilepsy

Staged anticonvulsant screening for chronic epilepsy

Automatic detection of epileptic seizures on the intra-cranial electroencephalogram of rats using reservoir computing

Long-term Continuous EEG Monitoring in Small Rodent Models of Human Disease Using the Epoch Wireless Transmitter System

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