Research Area:  Machine Learning

Abstract:

Epilepsy is the fourth most incident neurological disorder, for which approximately 50 million people are affected. The epilepsy is characterized by repeated seizures, occasioned by temporary electrical disturbances of the brain, and it is usually evidenced in four distinct events: pre-ictal, ictal, post-ictal, and interictal. This disorder can be diagnosed by electroencephalogram (EEG), which is the result of brain electrical activity monitoring. In the current work, we built multiclass classifiers in order to differentiate EEG segments among normal, interictal, and ictal classes. For this, the multitaper method was computed to generate power spectrum, spectrogram, and bispectrogram from each EEG segment, in which a total of 102 features were extracted. For classifier building, we used all-against-all approach to decompose the multiclass problem into binary subproblems, in which the following machine learning algorithms were applied: random forest, 1-nearest-neighbor, linear discriminant analysis (LDA), backpropagation based on multilayer perceptron (BP-MLP), k-means based on radial basis function network (KM-RBFN), and sequential minimal optimization. In the evaluation by ten-fold stratified cross-validation, the BP-MLP reached the highest accuracy (98.33%). Afterward, an extremely significant statistical difference was found among classifiers. In a post hoc test application, it was proved that the KM-RBFN classifier reached an inferior performance compared to LDA and BP-MLP predictive models. All classifiers built in this work achieved promising results, in which accuracy above 90% was obtained for classification of EEG segments.

Keywords:  

Author(s) Name:  Jefferson Tales Oliva; João Luís Garcia Rosa

Publisher name:  IEEE

DOI:  10.1109/IJCNN.2019.8852379

Volume Information:  

Paper Link:   https://ieeexplore.ieee.org/abstract/document/8852379