Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96823
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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.authorYanti, Ratnaen
dc.contributor.authorChua, Chua Kuangen
dc.contributor.authorNg, Eddie Yin-Kweeen
dc.contributor.authorTong, Louisen
dc.date.accessioned2013-07-17T01:36:45Zen
dc.date.accessioned2019-12-06T19:35:28Z-
dc.date.available2013-07-17T01:36:45Zen
dc.date.available2019-12-06T19:35:28Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationMartis, R. J., Acharya, U. R., Tan, J. H., Petznick, A., Yanti, R., Chua, C. K., et al. (2012). Application Of Empirical Mode Decomposition (Emd) For Automated Detection Of Epilepsy Using Eeg Signals. International Journal of Neural Systems, 22(6).en
dc.identifier.urihttps://hdl.handle.net/10356/96823-
dc.description.abstractEpilepsy is a global disease with considerable incidence due to recurrent unprovoked seizures. These seizures can be noninvasively diagnosed using electroencephalogram (EEG), a measure of neuronal electrical activity in brain recorded along scalp. EEG is highly nonlinear, nonstationary and non-Gaussian in nature. Nonlinear adaptive models such as empirical mode decomposition (EMD) provide intuitive understanding of information present in these signals. In this study a novel methodology is proposed to automatically classify EEG of normal, inter-ictal and ictal subjects using EMD decomposition. EEG decomposition using EMD yields few intrinsic mode functions (IMF), which are amplitude and frequency modulated (AM and FM) waves. Hilbert transform of these IMF provides AM and FM frequencies. Features such as spectral peaks, spectral entropy and spectral energy in each IMF are extracted and fed to decision tree classifier for automated diagnosis. In this work, we have compared the performance of classification using two types of decision trees (i) classification and regression tree (CART) and (ii) C4.5. We have obtained the highest average accuracy of 95.33%, average sensitivity of 98%, and average specificity of 97% using C4.5 decision tree classifier. The developed methodology is ready for clinical validation on large databases and can be deployed for mass screening.en
dc.language.isoenen
dc.relation.ispartofseriesInternational journal of neural systemsen
dc.rights© 2012 World Scientific Publishing Company.en
dc.titleApplication of Empirical Mode Decomposition (Emd) for automated detection of epilepsy using Eeg signalsen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen
dc.identifier.doi10.1142/S012906571250027Xen
item.grantfulltextnone-
item.fulltextNo Fulltext-
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