Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99399
Title: Application of intrinsic time-scale decomposition (ITD) to EEG signals for automated seizure prediction
Authors: Martis, Roshan Joy
Acharya, U. Rajendra
Tan, Jen Hong
Petznick, Andrea
Tong, Louis
Chua, Chua Kuang
Ng, Eddie Yin-Kwee
Keywords: DRNTU::Engineering::Mechanical engineering
Issue Date: 2013
Source: Martis, 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-.
Series/Report no.: International journal of neural systems
Abstract: Intrinsic 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.
URI: https://hdl.handle.net/10356/99399
http://hdl.handle.net/10220/17503
DOI: 10.1142/S0129065713500238
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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