Epileptic electroencephalography classification using embedded dynamic mode decomposition

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dc.citation.articleNumber036029en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleJournal of Neural Engineeringen_US
dc.citation.volumeNumber19en_US
dc.contributor.authorHellar, Jenniferen_US
dc.contributor.authorErfanian, Negaren_US
dc.contributor.authorAazhang, Behnaamen_US
dc.date.accessioned2022-07-06T18:09:14Zen_US
dc.date.available2022-07-06T18:09:14Zen_US
dc.date.issued2022en_US
dc.description.abstractObjective. Seizure prediction devices for drug-resistant epileptic patients could lead to improved quality of life and new treatment options, but current approaches to classification of electroencephalography (EEG) segments for early identification of the pre-seizure state typically require many features and complex classifiers. We therefore propose a novel spatio-temporal EEG feature set that significantly aids in separation and easy classification of the interictal and preictal states. Approach. We derive key spectral features from the embedded dynamic mode decomposition (EmDMD) of the brain state system. This method linearizes the complex spatio-temporal dynamics of the system, describing the dynamics in terms of a spectral basis of modes and eigenvalues. The relative subband spectral power and mean phase locking values of these modes prove to be good indicators of the preictal state that precedes seizure onset. Main results. We analyze the linear separability and classification of preictal and interictal states based on our proposed features using seizure data extracted from the CHB-MIT scalp EEG and Kaggle American Epilepsy Society Seizure Prediction Challenge intracranial EEG databases. With a light-weight support vector machine or random forest classifier trained on these features, we classify the preictal state with a sensitivity of up to 92% and specificity of up to 89%. Significance. The EmDMD-derived features separate the preictal and interictal states, improving classification accuracy and motivating further work to incorporate them into seizure prediction algorithms.en_US
dc.identifier.citationHellar, Jennifer, Erfanian, Negar and Aazhang, Behnaam. "Epileptic electroencephalography classification using embedded dynamic mode decomposition." <i>Journal of Neural Engineering,</i> 19, no. 3 (2022) IOP Publishing: https://doi.org/10.1088/1741-2552/ac7256.en_US
dc.identifier.digitalHellar_2022en_US
dc.identifier.doihttps://doi.org/10.1088/1741-2552/ac7256en_US
dc.identifier.urihttps://hdl.handle.net/1911/112675en_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 electroencephalography classification using embedded dynamic mode decompositionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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