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Epileptic seizure detection in EEG signal using machine learning techniques

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Abstract

Epilepsy is a well-known nervous system disorder characterized by seizures. Electroencephalograms (EEGs), which capture brain neural activity, can detect epilepsy. Traditional methods for analyzing an EEG signal for epileptic seizure detection are time-consuming. Recently, several automated seizure detection frameworks using machine learning technique have been proposed to replace these traditional methods. The two basic steps involved in machine learning are feature extraction and classification. Feature extraction reduces the input pattern space by keeping informative features and the classifier assigns the appropriate class label. In this paper, we propose two effective approaches involving subpattern based PCA (SpPCA) and cross-subpattern correlation-based PCA (SubXPCA) with Support Vector Machine (SVM) for automated seizure detection in EEG signals. Feature extraction was performed using SpPCA and SubXPCA. Both techniques explore the subpattern correlation of EEG signals, which helps in decision-making process. SVM is used for classification of seizure and non-seizure EEG signals. The SVM was trained with radial basis kernel. All the experiments have been carried out on the benchmark epilepsy EEG dataset. The entire dataset consists of 500 EEG signals recorded under different scenarios. Seven different experimental cases for classification have been conducted. The classification accuracy was evaluated using tenfold cross validation. The classification results of the proposed approaches have been compared with the results of some of existing techniques proposed in the literature to establish the claim.

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Notes

  1. EEG time series dataset http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3

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Acknowledgements

The authors would like to thank Dr. R.G. Andrzejak of the University of Bonn for providing the EEG time series dataset.

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Correspondence to Abeg Kumar Jaiswal.

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The authors declare that they have no competing interests.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The used dataset is publicly available.

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Jaiswal, A.K., Banka, H. Epileptic seizure detection in EEG signal using machine learning techniques. Australas Phys Eng Sci Med 41, 81–94 (2018). https://doi.org/10.1007/s13246-017-0610-y

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