Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96823
Title: Application of Empirical Mode Decomposition (Emd) for automated detection of epilepsy using Eeg signals
Authors: Martis, Roshan Joy
Acharya, U. Rajendra
Tan, Jen Hong
Petznick, Andrea
Yanti, Ratna
Chua, Chua Kuang
Ng, Eddie Yin-Kwee
Tong, Louis
Issue Date: 2012
Source: Martis, 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).
Series/Report no.: International journal of neural systems
Abstract: Epilepsy 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.
URI: https://hdl.handle.net/10356/96823
http://hdl.handle.net/10220/11625
DOI: 10.1142/S012906571250027X
Rights: © 2012 World Scientific Publishing Company.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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