ArticleAnnals of biomedical engineering2013
Automatic seizure detection in rats using Laplacian EEG and verification with human seizure signals.
Article in Annals of biomedical engineering, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins.Computational and mathematical methods in medicine · 2021Article
- Complex network based models of ECoG signals for detection of induced epileptic seizures in rats.Cognitive neurodynamics · 2019Article
- Solving the general inter-ring distances optimization problem for concentric ring electrodes to improve Laplacian estimation.Biomedical engineering online · 2018Article
- Automated Detection of Driver Fatigue Based on AdaBoost Classifier with EEG Signals.Frontiers in computational neuroscience · 2017Article
- Improving the Accuracy of Laplacian Estimation with Novel Variable Inter-Ring Distances Concentric Ring Electrodes.Sensors (Basel, Switzerland) · 2016Article
- Improving the accuracy of Laplacian estimation with novel multipolar concentric ring electrodes.Measurement : journal of the International Measurement Confederation · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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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Registered trials
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