Epileptic Seizure Prediction for Imbalanced Datasets

In this study, the methods used in the classification of imbalanced data sets were applied to EEG signals obtained from epilepsy patients and epileptic seizures were estimated. Firstly, the data set was balanced by using under-sampling, oversampling, and synthetic minority over-sampling technique and classified with Support Vector Machines. Then, the data set was classified using the Rusboost classifier without balancing. Classification results were compared with different criteria and the advantages and disadvantage of the methods were evaluated.

[1]  Andreas Schulze-Bonhage,et al.  Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients , 2014, Comput. Methods Programs Biomed..

[2]  Awais M. Kamboh,et al.  A robust approach towards epileptic seizure detection , 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Khawar Khurshid,et al.  Dynamic Mode Decomposition Based Epileptic Seizure Detection from Scalp EEG , 2018, IEEE Access.

[6]  Jiawei Yang,et al.  Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram , 2018, Neural Networks.

[7]  Francisco Sales,et al.  A Realistic Seizure Prediction Study Based on Multiclass SVM , 2017, Int. J. Neural Syst..

[8]  C. Teixeira,et al.  Preprocessing effects of 22 linear univariate features on the performance of seizure prediction methods , 2013, Journal of Neuroscience Methods.

[9]  Taghi M. Khoshgoftaar,et al.  RUSBoost: A Hybrid Approach to Alleviating Class Imbalance , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Jasmin Kevric,et al.  Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction , 2018, Biomed. Signal Process. Control..