Binary and multiclass classifiers based on multitaper spectral features for epilepsy detection

Epilepsy is one of the most common neurological disorders that can be diagnosed by means of electroencephalogram (EEG) analysis, in which the following epileptic events can be observed: pre-ictal, ictal, post-ictal, and interictal. In this paper, we present a novel method for epilepsy detection employing binary and multiclass classifiers. For feature extraction, a total of 105 measurements were extracted from power spectrum, spectrogram, and bispectrogram. For classifier building, widely known machine learning algorithms were used. Our method was applied in a publicly available EEG database. As a result, BP-MLP (backpropagation based on multilayer perceptron) and SMO_Pol (sequential minimal optimization supported by the polynomial kernel) algorithms reached the highest accuracy for binary (100%) and multiclass (98%) classification problems. Subsequently, statistical tests did not find a better performance model. In the evaluation based on confusion matrices, it was also impossible to identify a classifier that stands out concerning other models for EEG classification. In comparison to related words, our predictive models reached competitive results.

Automated summary

A novel method for epilepsy detection using binary and multiclass classifiers was presented and validated on an EEG database. BP-MLP and SMO_Pol algorithms showed the highest accuracy for binary and multiclass classification problems.