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Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals

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Abstract

Automated detection of seizures is still a challenging problem. This study presents an approach to detect seizure segments in Laplacian electroencephalography (tEEG) recorded from rats using the tripolar concentric ring electrode (TCRE) configuration. Three features, namely, median absolute deviation, approximate entropy, and maximum singular value were calculated and used as inputs into two different classifiers: support vector machines and adaptive boosting. The relative performance of the extracted features on TCRE tEEG was examined. Results are obtained with an overall accuracy between 84.81 and 96.51%. In addition to using TCRE tEEG data, the seizure detection algorithm was also applied to the recorded EEG signals from Andrzejak et al. database to show the efficiency of the proposed method for seizure detection.

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Acknowledgments

The authors would like to thank lab members Xiang Liu, Liling Wang, and Oleksandr Makeyev for performing the experiments to collect the data. The project described was supported in part by Award Number R21NS061335 from the National Institute of Neurological Disorders and Stroke. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health.

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Authors and Affiliations

  1. Department of Electrical, Computer, and Biomedical Engineering Department, University of Rhode Island, 4, East Alumni Avenue, Kingston, RI, 02881, USA

    Amal Feltane, G. Faye Boudreaux-Bartels & Walter Besio

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  1. Amal Feltane
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  2. G. Faye Boudreaux-Bartels
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  3. Walter Besio
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Correspondence to Walter Besio.

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Associate Editor Berj L. Bardakjian oversaw the review of this article.

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Feltane, A., Faye Boudreaux-Bartels, G. & Besio, W. Automatic Seizure Detection in Rats Using Laplacian EEG and Verification with Human Seizure Signals. Ann Biomed Eng 41, 645–654 (2013). https://doi.org/10.1007/s10439-012-0675-4

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  • DOI: https://doi.org/10.1007/s10439-012-0675-4

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