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

Author(s)

Oliva, Jefferson Tales ^[1] ; Garcia Rosa, Joao Luis ^[2]

Affiliation

^[1] Univ Tecnol Fed Parana, Acad Dept Informat, Pato Branco, Parana - Brazil

^[2] Univ Sao Paulo, Dept Comp Sci, Sao Carlos, SP - Brazil

• Document type: Journal article
• Source: Biomedical Signal Processing and Control; v. 66, APR 2021.
• Web of Science Citations: 1

Abstract

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. (AU)

• FAPESP's process: 16/02555-8 - Development of algorithms and computational techniques for application in brain-computer interfaces
• Grantee: João Luís Garcia Rosa
• Support Opportunities: Regular Research Grants