Differentiation between Normal and Interictal EEG Using Multitaper Spectral Classifiers

Abstract

According to World Health organization, epilepsy is one of the most common chronic neurological disorder, affecting approximately 50 million people. This infirmity presents four kinds of events: pre-ictal, ictal, post-ictal, and interictal. Epilepsy can be diagnosed through electroencephalogram (EEG). The interictal activity, for example, on an EEG, is widely accepted as an epilepsy symptom. However, the differentiation between normal and interictal EEG segments is difficult because they can have similar patterns. Also, EEG from patients with epilepsy can contain normal events. In this work, we built classifiers to differentiate between normal and interictal EEG. Our proposed process was applied in a set of 200 EEG segments. For this, power spectrum (PS) was computed for each signal, and 18 measures were extracted from PS considering five frequency bands: delta, theta, alpha, beta, and entire frequency range (between lower delta and higher beta frequency range). Thus, from each PS, 90 features were extracted. The following machine learning methods were applied to build the classifiers: random forest, INN, naive Bayes (NB), MLP, and SVM. In the evaluation by cross-validation approach, a statistically significant difference was not found among classifiers, whose error values resulted in the p-value of 0.1089 by Friedman test. On the other hand, by confusion matrices and their parameters, it was found that the NB classifier reached the best performance to detect normal EEG segments. For detection of interictal activity, the MLP and SVM classifiers achieved the best results. All classifiers built in this work reached promising results for differentiation between normal and interictal EEG patterns.