Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99399
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dc.contributor.authorMartis, Roshan Joyen
dc.contributor.authorAcharya, U. Rajendraen
dc.contributor.authorTan, Jen Hongen
dc.contributor.authorPetznick, Andreaen
dc.contributor.authorTong, Louisen
dc.contributor.authorChua, Chua Kuangen
dc.contributor.authorNg, Eddie Yin-Kweeen
dc.date.accessioned2013-11-08T06:57:45Zen
dc.date.accessioned2019-12-06T20:06:48Z-
dc.date.available2013-11-08T06:57:45Zen
dc.date.available2019-12-06T20:06:48Z-
dc.date.copyright2013en
dc.date.issued2013en
dc.identifier.citationMartis, R. J., Acharya, U. R., Tan, J. H., Petznick, A., Tong, L., Chua, C. K., et al. (2013). Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction. International journal of neural systems, 23(05), 1350023-.en
dc.identifier.urihttps://hdl.handle.net/10356/99399-
dc.identifier.urihttp://hdl.handle.net/10220/17503en
dc.description.abstractIntrinsic time-scale decomposition (ITD) is a new nonlinear method of time-frequency representation which can decipher the minute changes in the nonlinear EEG signals. In this work, we have automatically classified normal, interictal and ictal EEG signals using the features derived from the ITD representation. The energy, fractal dimension and sample entropy features computed on ITD representation coupled with decision tree classifier has yielded an average classification accuracy of 95.67%, sensitivity and specificity of 99% and 99.5%, respectively using 10-fold cross validation scheme. With application of the nonlinear ITD representation, along with conceptual advancement and improvement of the accuracy, the developed system is clinically ready for mass screening in resource constrained and emerging economy scenarios.en
dc.language.isoenen
dc.relation.ispartofseriesInternational journal of neural systemsen
dc.subjectDRNTU::Engineering::Mechanical engineeringen
dc.titleApplication of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure predictionen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doihttp://dx.doi.org/10.1142/S0129065713500238en
item.grantfulltextnone-
item.fulltextNo Fulltext-
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