Epileptic seizure prediction using spectral width of the covariance matrix

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dc.citation.articleNumber026029en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleJournal of Neural Engineeringen_US
dc.citation.volumeNumber19en_US
dc.contributor.authorEPMoghaddam, Dorsaen_US
dc.contributor.authorSheth, Sameer A.en_US
dc.contributor.authorHaneef, Zulfien_US
dc.contributor.authorGavvala, Jayen_US
dc.contributor.authorAazhang, Behnaamen_US
dc.date.accessioned2022-04-28T14:28:38Zen_US
dc.date.available2022-04-28T14:28:38Zen_US
dc.date.issued2022en_US
dc.description.abstractObjective. Epilepsy is a common neurological disorder in which patients suffer from sudden and unpredictable seizures. Seizures are caused by excessive and abnormal neuronal activity. Different methods have been employed to investigate electroencephalogram (EEG) data in patients with epilepsy. This paper introduces a simple yet accurate array-based method to study and predict seizures. Approach. We use the CHB-MIT dataset (all 24 cases), which includes scalp EEG recordings. The proposed method is based on the random matrix theory. After applying wavelet decomposition to denoise the data, we analyze the spatial coherence of the epileptic recordings by looking at the width of the covariance matrix eigenvalue distribution at different time and frequency bins. Main results. We train patient-specific support vector machine classifiers to distinguish between interictal and preictal data with high performance and a false prediction rate as low as 0.09 h−1. The proposed technique achieves an average accuracy, specificity, sensitivity, and area under the curve of 99.05%, 93.56%, 99.09%, and 0.99, respectively. Significance. Our proposed method outperforms state-of-the-art works in terms of sensitivity while maintaining a low false prediction rate. Also, in contrast to neural networks, which may achieve high performance, this work provides high sensitivity without compromising interpretability.en_US
dc.identifier.citationEPMoghaddam, Dorsa, Sheth, Sameer A., Haneef, Zulfi, et al.. "Epileptic seizure prediction using spectral width of the covariance matrix." <i>Journal of Neural Engineering,</i> 19, no. 2 (2022) IOP Publishing: https://doi.org/10.1088/1741-2552/ac6063.en_US
dc.identifier.digitalEPMoghaddam_2022en_US
dc.identifier.doihttps://doi.org/10.1088/1741-2552/ac6063en_US
dc.identifier.urihttps://hdl.handle.net/1911/112165en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.rightsOriginal content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.titleEpileptic seizure prediction using spectral width of the covariance matrixen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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